{"id":4597,"date":"2023-03-12T22:06:13","date_gmt":"2023-03-12T19:06:13","guid":{"rendered":"http:\/\/conferences.tiu.edu.iq\/icad\/?page_id=4597"},"modified":"2023-03-12T22:06:13","modified_gmt":"2023-03-12T19:06:13","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/conferences.tiu.edu.iq\/kif\/accepted-papers\/","title":{"rendered":"Accepted Papers"},"content":{"rendered":"

\nAbstracts of Accepted Papers<\/h1>\n

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Monday, December 15, 2025<\/strong><\/p>\n

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Plenary Session 1: Finance, Business and AI Developments<\/strong><\/p>\n

(Monday, December 15, 2025, 10h30 \u2013 11h45)<\/em><\/p>\n<\/div><\/div>\n

\n

Paper #: PL1-KSp1<\/em><\/p>\n

Dual-Layer Crisis Prediction Model (DLCPM): An Integrated Early-Warning and Precise-Timing<\/strong><\/h2>\n

Mahmood M. Dagher1,* <\/sup>and Abbas K. Saddam2<\/sup><\/p>\n

1<\/sup><\/em>Head of Integrate Globals, Iraq Baghdad<\/em><\/p>\n

2<\/sup><\/em>Head of Edumonde Research, UAE, Dubai<\/em><\/p>\n

*Contact: dr_m_daghir@yahoo.com<\/em><\/p>\n

\u00a0<\/strong><\/p>\n

Abstract:<\/strong><\/p>\n

Financial crises often unfold through a sequence of mounting imbalances, followed by abrupt market breakdowns. While traditional early-warning systems (EWS) can flag the likelihood of crises months in advance, they usually fail to provide precise timing for the actual onset of turmoil. This research introduces the Dual-Layer Crisis Prediction Model (DLCPM), a novel framework that integrates an Early-Warning Index (EWI) based on leading macro-financial indicators with a Timing-Trigger Index (TTI) built from high-frequency market stress measures. The EWI captures structural vulnerabilities using variables such as yield curve inversion, monetary tightening, PMI downturns, debt dynamics, and reserve adequacy. Once EWI reaches critical thresholds, the TTI becomes activated, monitoring fast-moving indicators such as VIX, funding spreads, CDS, and shipping indices to identify the precise crisis timing. A CUSUM change-point algorithm is then applied to TTI to pinpoint the crisis onset date (T\u2080). Backtesting on the Global Financial Crisis (2008), the oil price collapse (2014), and the COVID-19 crisis (2020) demonstrates that DLCPM not only provides earlier warnings than conventional single-indicator models but also accurately identifies the timing of crisis eruptions. The findings suggest that DLCPM can serve as a valuable policy tool for central banks, regulators, and institutional investors seeking both anticipatory signals and actionable timing of crises.<\/p>\n

Keywords:<\/strong> Yield curve, financial crises, VIX, Monetary tightening, Early-Warning Systems (EWS), Timing-Trigger Index (TTI), Macro-financial indicators, Oil price collapse<\/p>\n<\/div><\/div>\n

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Paper #: PL1-KSp2<\/em><\/p>\n

AI Policies and National Strategy of AI in Iraq<\/strong><\/h2>\n

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Dhiya Al-Jumeily<\/p>\n

Liverpool John Moores University, eSystems Engineering Society, UK<\/em><\/p>\n

Contact: d.aljumeily@ieee.org<\/em><\/p>\n

\u00a0<\/em><\/strong><\/p>\n

Abstract:<\/strong><\/p>\n

Artificial intelligence (AI) popularity have escalated over the last few years and its application have become popular in many disciplines including major disciplines such as healthcare and the environment. Other Industry 4.0 technologies like automation, sensing, and IoT helped boost AI adoption. In this regard, AI and machine learning (ML) algorithms have been focused on speeding up data processing and predictions, which were crucial in many applications like manufacturing, driving cars, delivering goods, dispensing medicines and food, performing surgeries, and more. AI and ML are different but related terms. Machines using AI can think like humans and perform human activities. ML trains robots to classify or predict without intelligence. So, AI is a subset of ML but not vice versa.<\/p>\n

AIML applications have gained popularity over the last few years especially post-Covid-19 pandemic. Thus, the Covid-19 pandemic urged the need for automation and using robotics especially for vulnerable individuals. When it first emerged, algorithms became popular and particularly deep learning algorithms and deep transfer learning algorithms. Recently, generative AI surged and its use surpassed previous algorithms. It is worthnoting that AIML algorithms did not start post-pandemic. In fact, the first algorithms emerged in the 8th<\/sup> and 9th<\/sup> centuries in the Islamic golden age et the House of Wisdom in Baghdad.<\/p>\n

The House of Wisdom in Baghdad hosted many scholars from around the world in numerous disciplines. One of these scholars, Mohammad bin Musa Al-Khawarizmi, have created the algorithms (Al-Khawarizmiyat) that were named after him. Algorithms informed about patterns in data and made predictions based on data. This placed many focus on algorithms over the years. Hence accuracy and precision of algorithms have always been a key question in any evaluation.<\/p>\n

This marginalised the role of datasets that did not receive many attention until the last five years where big data became significant. This was facilitated by high performance computing, cloud computing, blockchain and IoT technologies that not only facilitated the collection of big data but also big data analysis in short time. However, the challenges related to datasets have been undermined until Covid-19 pandemic. Hence the pandemic shifted the focus of research (particularly healthcare research) to be relating to Covid-19 prediction and\/or treatment. Specifically in cases of Covid -19 predictions, there was major focus on accuracy of prediction, and this triggered the revision of datasets. Hence, models trained with poor datasets gave poor prescribe and vice versa. Yet until now there are no guidances for datasets as algorithms apart from few regulations such as the European Union legislation on data protection, ethics as big data.<\/p>\n

Considering the aforementioned highlights, this presentation will discuss regulatory and ethical challenges relating to handling and analysing big data using AI. It will then conclude by a case study of big data in making healthcare decision. It will highlight the emergence of AI and present some of its successful achievements in numerous disciplines. Challenges related to applying AI algorithms and choice\/use of datasets will be discussed.<\/em><\/p>\n<\/div><\/div>\n

\n

Session 1: Education and Signal Processing<\/strong><\/p>\n

(Monday, December 15, 2025, 12h00-13h30)<\/p>\n<\/div><\/div>\n

\n

Paper #: S1-KSp3<\/em><\/p>\n

Artificial Intelligence in Education \u2013 Transforming Teaching, Learning, and Administration<\/strong><\/h2>\n

Ahmad Jammal<\/p>\n

Arab International Foundation for Education Development, Lebanon<\/em><\/p>\n

Email: a-jammal@ieee.org<\/em><\/p>\n

Abstract:<\/strong><\/p>\n

Artificial Intelligence (AI) is often compared to the industrial or digital revolutions, but its scope is far greater. While earlier technologies amplified human physical and computational abilities, AI amplifies something deeper: our capacity to think, decide, and create. In doing so, AI is not just changing how we work or communicate, it is redefining what it means to be human in an age of intelligent machines. AI is not merely a tool; it\u2019s a cultural force. It challenges traditional paradigms of identity, creativity, and morality while creating opportunities for cultural renewal and intercultural dialogue. The societies that embrace AI as both a technological and cultural phenomenon will lead the next wave of human-centered innovation. AI transforms education into a smart, adaptive, and inclusive ecosystem enhancing teaching, empowering learners, and optimizing institutional performance while emphasizing the continued importance of human values and ethics.<\/p>\n

Artificial Intelligence transforms education by enhancing teaching effectiveness, personalizing learning experiences, and optimizing institutional administration. In teaching, AI supports personalized and data-driven pedagogy, intelligent tutoring systems, and automation of grading and curriculum design. These innovations enable educators to focus on higher-level mentoring, creativity, and student engagement rather than routine tasks. For learners, AI fosters adaptive, self-paced, and inclusive education. Intelligent platforms provide real-time feedback, track progress, and accommodate diverse learning styles and abilities. Through accessibility tools like speech recognition and translation, AI promotes equity and supports students with disabilities or language barriers. Moreover, by engaging with AI technologies, students develop essential 21st-century skills\u2014digital literacy, creativity, and ethical reasoning. At the administrative level, AI strengthens institutional efficiency through predictive analytics, smart admissions, and data-driven decision-making. It enhances quality assurance by mapping learning outcomes, identifying performance trends, and streamlining accreditation processes. Despite its promise, AI raises ethical concerns related to privacy, bias, and human oversight. Its successful integration requires robust governance, equitable access, and capacity-building for educators and students. Policymakers should develop national AI strategies for education, invest in infrastructure, embed AI ethics in curricula, and ensure inclusive implementation. Ultimately, AI should augment. – not replace – human intelligence. When integrated responsibly, it can create a more inclusive, efficient, and innovative education ecosystem that aligns with global goals for quality and lifelong learning.<\/p>\n

Keywords:<\/strong> Artificial Intelligence, Education, Data-driven Pedagogy, Policymakers, Ethical issues.<\/p>\n<\/div><\/div>\n

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Paper #: S1-1<\/em><\/p>\n

Leveraging AI for Optimising Student Workload and Enhancing Well-being in Higher Education<\/strong><\/h2>\n

\u00a0<\/em><\/p>\n

Waleed Al-Nuaimy<\/em><\/p>\n

School of Engineering, University of Liverpool, UK<\/em><\/p>\n

Contact: wax@liverpool.a.uk, phone +44 151 794 4512<\/em><\/p>\n

\u00a0<\/u><\/em><\/p>\n

Abstract: <\/strong><\/p>\n

The escalating demands of higher education are increasingly linked to student stress and inconsistent academic performance. Traditional workload estimations, based on static credit hours, fail to capture the dynamic and individualised nature of student effort. This disparity often results in cyclical workloads with intense peaks around assessment deadlines, negatively impacting student well-being and learning outcomes. To address this, we present an intelligent, data-driven framework designed to model, predict, and optimise student academic workload in real-time.<\/p>\n

Our system moves beyond conventional metrics by integrating comprehensive course data such as timetables and assessment schedules, with personalised student study profiles. Using an algorithmic approach, the framework simulates a student\u2019s entire academic schedule to analyse workload intensity and variability. A key innovation is the calculation of a “pain score,” a metric that quantifies the congestion of a student\u2019s workload, allowing for the proactive identification of high-stress periods. Based on this analysis, the system generates dynamic and personalised study plans that distribute effort more evenly, mitigating deadline-related anxiety.<\/p>\n

The framework provides students with clear visualisations to aid planning and offers educators valuable policy informatics to inform curriculum design and prevent systemic student overload. A continuous feedback loop, where students validate the accuracy of workload predictions, ensures the model refines itself over time. While currently data-driven, the system is architected for deep AI integration to further automate profile detection, enhance predictive accuracy, and optimise scheduling. This work presents a proactive model for workload management, demonstrating a clear pathway to leveraging technology to create a more supportive and sustainable learning environment, ultimately enhancing both student well-being and academic success.<\/p>\n

Keywords<\/strong>: AI in Education, Student Workload, Student Well-being, Personalized Learning, Learning Analytics, Educational Technology, Curriculum Design.<\/p>\n<\/div><\/div>\n

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Paper #: S1-2<\/em><\/p>\n

Entangled Intelligence: Exploring Quantum Neural Networks for Next-Gen AI<\/strong><\/p>\n

Ezideen A Hasso<\/p>\n

Tishk International University, Erbil, Iraq<\/em><\/p>\n

Contact: ezideen.hasso@tiu.edu.iq<\/em><\/p>\n

Abstract:<\/strong><\/p>\n

Quantum Neural Networks (QNNs) represent a promising and innovative convergence of quantum computing and machine learning, aiming to address complex, high-dimensional, and non-linear problems that are often intractable for purely classical systems. Unlike traditional neural networks, QNNs leverage the principles of superposition and entanglement, enabling them to potentially process information in fundamentally new ways and offer advantages in terms of representational power and computational efficiency. In this study, we present the design and implementation of a two-qubit QNN developed through MATLAB\u2019s Quantum Support Package and integrated with IBM\u2019s Qiskit Runtime. The framework demonstrates a hybrid quantum\u2013classical workflow tailored to binary classification tasks. Specifically, a parameterized quantum circuit (PQC) is employed to model the XOR problem, which is a canonical benchmark for evaluating the ability of machine learning models to solve non-linearly separable functions. Classical data inputs are embedded into quantum states, and the trainable quantum gates within the PQC are optimized iteratively using classical backpropagation methods, thereby uniting the strengths of both computational paradigms. The QNN was initially tested through local MATLAB-based simulation to ensure correctness and performance stability. Subsequently, the model was executed on actual IBM quantum hardware via the Qiskit Runtime, thereby assessing its robustness under realistic noisy intermediate-scale quantum (NISQ) conditions. Results confirm that the two-qubit QNN successfully learns the structure of the XOR dataset, providing accurate classifications and demonstrating that even low-qubit quantum systems can perform meaningful machine learning tasks. This work not only validates the practical viability of hybrid QNN approaches but also establishes a foundation for future research on extending such models to larger, more complex datasets and applications. It highlights how accessible quantum resources, when combined with classical optimization, can open pathways toward scalable quantum-enhanced machine learning.<\/p>\n

Keywords<\/strong>: Quantum Neural Networks (QNN), Quantum AI, Qubits, Quantum machine learning, Quantum gates, and Hybrid quantum-classical systems.<\/p>\n

References:<\/strong><\/p>\n[1] M. A.Hafeez, A. Munir, and H.Ullah, (2024).\u201cH-QNN: A Hybrid Quantum\u2013Classical Neural Network for Improved Binary Image Classification, \u201cAI, 5(3):1462-1481,2024.doi:10.3390\/ai5030070.MDPI.<\/p>\n[2] S.Markidis, S. (2023).\u201cProgramming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies. Entropy,\u201d25(4):694, 2023. Doi:10.3390\/e25040694.<\/p>\n[3] Y. Chenand A. Khaliq, \u201cQuantum Recurrent Neural Networks: Predicting the Dynamics of Oscillatory and Chaotic Systems. Algorithms, 17(4), 163, 2024.Doi:10.3390\/a17040163. MDPI.<\/p>\n[4] U. Singh, A. Z. Goldberg and K. Heshami, \u201cCoherent feed-forward quantum neural network. Quantum Machine Intelligence,\u201d6,89, 2024.Doi:10.1007\/s42484-024-00222-8.<\/p>\n[5] S. Sahil, D. Nandhini, et al. \u201cHybrid Quantum Neural Networks: Harnessing Dressed Quantum Circuits for Enhanced Tsunami Prediction via Earthquake Data Fusion,\u201d Shivanya Shomir Dutta, EPJ Quantum Technology,12(4),2025.<\/p>\n[6] Y. Zhang, and H. Lu, \u201cReliability Research on Quantum Neural Networks,\u201d Electronics, 13(8):1514.2024.Doi:10.3390\/electronics13081514. MDPI.<\/p>\n<\/div><\/div>\n

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Paper #: S1-3<\/em><\/p>\n

Classifying ECG Signals Using Wavelet Transforms and Convolutional Neural Networks<\/strong><\/p>\n

Munaf Salim Najim AL-Din*<\/sup> and Zamzam Salim Saif Alhuseini<\/p>\n

Department of Electrical and Computer Engineering,<\/p>\n

College of Engineering and Architecture, University of Nizwa, Nizwa, Oman<\/p>\n

*Contact: munaf@unizwa.edu.om<\/p>\n

\u00a0<\/u><\/p>\n

Abstract: <\/strong><\/p>\n

Cardiovascular diseases remain a leading cause of mortality worldwide, necessitating efficient and accurate diagnostic methods to support early detection and timely intervention. This project presents a robust deep learning framework for classifying electrocardiogram (ECG) signals by integrating time-frequency analysis using Continuous Wavelet Transform (CWT) with Convolutional Neural Networks (CNNs). Raw ECG signals obtained from the MIT-BIH Arrhythmia Database, MIT-BIH Normal Sinus Rhythm Database, and BIDMC Congestive Heart Failure Database were preprocessed to remove baseline wander and high-frequency noise, then standardized to a uniform sampling rate of 128 Hz to ensure consistency across datasets. Each signal segment was transformed into a scalogram using CWT, capturing its time-frequency characteristics and enabling more effective and discriminative feature representation compared to traditional time-domain or frequency-domain approaches. These scalogram images were classified using multiple pre-trained CNN architectures, including AlexNet, GoogleNet, ResNet50, InceptionV3, EfficientNet-B0, VGG19, and SqueezeNet, through transfer learning implemented in MATLAB. Training was performed with optimized hyperparameters, data augmentation techniques, and five-fold cross-validation to improve model generalization and reduce overfitting. Experimental results demonstrated that EfficientNet-B0 and ResNet50 achieved superior accuracy, sensitivity, and specificity in detecting normal and abnormal heart rhythms, including arrhythmias and congestive heart failure, outperforming conventional ECG classification methods. Confusion matrices and receiver operating characteristic (ROC) curves further confirmed the robustness and reliability of the proposed models. The proposed system showcases the potential of combining wavelet-based feature extraction with advanced deep CNN models for automated, high-accuracy ECG signal classification, offering a scalable and interpretable solution for real-world applications. Its integration into clinical workflows could facilitate early diagnosis, reduce clinician workload, and improve patient outcomes. This approach represents a significant step toward intelligent cardiovascular monitoring and decision support systems, making it a valuable tool for modern healthcare environments focused on precision medicine and proactive disease management.<\/p>\n

Keywords<\/strong>: Electrocardiogram (ECG), Wavelet Transforms, Convolutional Neural Networks.<\/p>\n

References:<\/strong><\/p>\n[1] World Heart Federation, World Heart Report 2023, World Heart Federation, 2023. [Online]. Available:https:\/\/world-heart-federation.org\/wp-content\/uploads\/World-Heart-Report-2023.pdf.<\/p>\n[2] \u00a0Jahangir, M.\u202fN.\u202fIslam, M.\u202fS.\u202fIslam, and M.\u202fM.\u202fIslam, \u201cECG\u2011based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning,\u201d BMC Cardiovascular Disorders, vol.\u202f25, art.\u202fno.\u202f260, \u202f7,\u202f2025.<\/p>\n[3] Musa, N., Gital, A.Y., Aljojo, N. et al, \u201cA systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram,\u201d Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 9677\u20139750, 2022.<\/p>\n[4] \u00a0T. Wang, C. Lu, Y. Sun, M. Yang, C. Liu, and C. Ou, \u201cAutomatic ECG classification using continuous wavelet transform and convolutional neural network,\u201d Entropy, vol. 23, no. 1, p. 119, Jan. 2021.<\/p>\n[5] S. C. Mohonta, M.A. Motin, and D. Kumar, \u201cElectrocardiogram Based Arrhythmia Classification Using Wavelet Transform with Deep Learning Model,\u201d Sensing and Bio-Sensing Research, vol. 37, 100502, 2022.<\/p>\n[6] C. Lakshminarayan and T. Basil, \u201cFeature Extraction and Automated Classification of Heartbeats by Machine Learning,\u201d arXivpre print arXiv:1607.03822, 2016.<\/p>\n[7] S. Chatterjee, R. S. Thakur, R. N. Yadav, L. Gupta, and D. K. Raghuvanshi, \u201cReview of noise removal techniques in ECG signals,\u201d Iet Signal Processing, vol. 14, no.9, pp. 569\u2013590, 2020.<\/p>\n<\/div><\/div>\n

\n

Paper #: S1-4<\/em><\/p>\n

Artificial Hand Force Estimation System Based on Electromyography Signals with Metaheuristic Optimization Algorithm<\/strong><\/h2>\n

Sami Abduljabbar Rashid1<\/sup>, Yousif Al Mashhadany1<\/sup>, Mohanad A. Al-askari1<\/sup>, Sameer Algburi2<\/sup><\/p>\n

1<\/sup>Biomedical Engineering Research Center University of Anbar, Anbar, Iraq<\/p>\n

2<\/sup>College of Engineering, Al-Kitab University, Kirkuk, Iraq<\/p>\n

*Contact: sami.abduljabbar.rashid@uoanbar.edu.iq, Phone +964 -7815626361<\/p>\n

\u00a0<\/strong><\/p>\n

Abstract:<\/strong><\/p>\n

Electromyography (EMG) signals are widely applied in clinical diagnostics to identify neuromuscular disorders. However, their non-stationary nature and susceptibility to noise introduce challenges in achieving reliable classification and accurate feature extraction. To address these limitations, this study proposes an Artificial Hand Force Estimation System based on EMG signals integrated with a metaheuristic optimization approach, termed AHFEMO. The model leverages Discrete Wavelet Transform (DWT) for multi-resolution feature extraction, Shapley Additive Explanations (SHAP) for interpretability, Support Vector Machine (SVM) for classification, and a Genetic Algorithm (GA) for hyperparameter tuning and feature optimization. ANOVA testing is further employed to ensure statistical significance in selected features. The overall workflow of the proposed model is presented in Fig1. Experimental analysis was performed using EMG signals collected from control, myopathic, and neuropathic subjects. The proposed AHFEMO framework achieved superior results in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), accuracy, precision, recall, and F1-score compared with baseline models such as AIEEMG, ISEEAI, and DISEAI. Specifically, AHFEMO reduced the MSE for healthy participants from 1.0 to 0.91 and improved classification accuracy to 0.72, with recall and F1-score values of 0.88 and 0.95, respectively. Confusion matrix analysis confirmed that the proposed method minimized false positives and false negatives, achieving a balanced performance across different patient groups. The findings demonstrate that combining DWT with explainable machine learning and evolutionary optimization significantly enhances the reliability of EMG-based diagnostics. The hybrid approach not only improves accuracy but also reduces computational complexity by eliminating redundant features. These outcomes highlight the potential of the AHFEMO model for advancing prosthetic control and medical diagnostic systems. Future work will focus on adapting the framework for real-time applications in wearable devices to support continuous neuromuscular monitoring.<\/p>\n

Keywords: <\/strong>Electromyography (EMG), Discrete Wavelet Transform (DWT), Shapley Additive Explanations (SHAP), Support Vector Machine (SVM), Genetic Algorithm (GA), Biomedical Signal Processing.<\/p>\n

References:<\/strong><\/p>\n[1] \u00a0N. C\u00e1rdenas-Bola\u00f1o, A. Polo, et al., “Implementation of an Intelligent EMG Signal Classifier Using Open-Source Hardware, “Computers, vol. 12, p. 263, 2023, doi: 10.3390\/computers12120263.<\/p>\n[2] \u00a0Y. I. Al-Mashhadany, “Inverse Kinematics Problem (IKP) of 6-DOF Manipulator by Locally Recurrent Neural Networks (LRNNs),” in Proc. Int. Conf. on Management and Service Science (ICMSS), 2010, pp. 1\u20135, doi:10.1109\/ICMSS.2010.5577613.<\/p>\n[3] \u00a0M. S. Ahmed, A. Ramizy, and Y. Al-Mashhadany, “Analysis of Real Measurement for EMG Signal Based on Surface Traditional Sensors,” in Lecture Notes in Networks and Systems, vol. 1138, Cham: Springer, 2024, doi:10.1007\/978-3-031-70924-1_19.<\/p>\n[4] \u00a0C. L. Kok, C. K. Ho, et al., “Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control, “Applied Sciences, vol. 14, p. 5784, 2024, doi: 10.3390\/app14135784.<\/p>\n[5] \u00a0M. Z. Al-Faiz and Y. I. Al-Mashhadany, “Analytical Solution for Anthropomorphic Limbs Model (IK of Human Arm),” in Proc. IEEE Symp. on Industrial Electronics & Applications (ISIEA), Kuala Lumpur, Malaysia, 2009, pp. 684\u2013689, doi: 10.1109\/ISIEA.2009.5356374<\/p>\n<\/div><\/div>\n

\n

Paper #: S1-5<\/em><\/p>\n

Deep Learning-Enabled Equalization for High-Mobility OTFS Wireless Systems<\/strong><\/h2>\n

\u00a0<\/strong><\/p>\n

Sura H. Khadem1*<\/sup> , Thamer M. Jamel2<\/sup>, and Hassan F. khazaal3<\/sup><\/p>\n

1,2<\/sup>University of Technology, College of Communication Engineering, Baghdad, Iraq<\/p>\n

3<\/sup>Department of Electrical Engineering, Wasit University<\/p>\n

*Contact: coe.23.02@grad.uotechnology.edu.iq<\/a><\/p>\n

\u00a0<\/strong><\/p>\n

Abstract:<\/strong><\/p>\n

Orthogonal Time Frequency Space (OTFS) modulation offers a powerful solution for high-mobility wireless communication by effectively describing channels in the delay-Doppler domain, where sparsity and gradual variation are prevalent. However, achieving effective equalization in OTFS for doubly selective fading channels, which exhibit both time and frequency selectivity, remains a significant challenge. Traditional equalization techniques, such as linear Minimum Mean Square Error (MMSE) and iterative message-passing methods, face limitations due to high computational demands and increased sensitivity to imperfections in channel state information (CSI). This paper introduces a novel and comprehensive deep learning (DL)-based equalization framework for OTFS, leveraging convolutional neural networks (CNNs) to address these critical issues. Our proposed CNN-based equalizer is designed to overcome the computational burden and sensitivity inherent in conventional approaches. Extensive simulations were conducted across a variety of practical high-mobility channel models, including Extended Vehicular A (EVA), Extended Typical Urban (ETU), Unmanned Aerial Vehicle (UAV), TDL-C, and other generic doubly selective channels. The results consistently demonstrate that the CNN-based equalizer achieves significant improvements in Bit Error Rate (BER) performance. Furthermore, it exhibits enhanced tolerance to channel uncertainties and considerably reduces computational complexity compared to traditional equalization methods. This advancement paves the way for more robust and efficient OTFS systems in dynamic wireless environments.<\/p>\n

Keywords:<\/strong> Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), OTFS, Equalization, Bit Error Rate (BER), High-Mobility Channels.<\/em><\/strong><\/p>\n

References:<\/strong><\/p>\n[1] Bo Ai, Yunlong\u00a0Lu, Yuguang\u00a0Fang, Dusit\u00a0Niyato, Ruisi\u00a0He, Wei\u00a0Chen, Jiayi\u00a0Zhang,\u00a0Guoyu\u00a0Ma,\u00a0Yong\u00a0Niu, and\u00a0Zhangdui\u00a0Zhong,\u201d 6G-Enabled Smart Railways\u201d, arXiv:2505.12946v1 [eess.SY] 19 May 2025.<\/p>\n[2] B. Sathish Kumar,\u00a0K. R. Shankar Kumar,\u00a0R. Radhakrishnan, \u201c
\nAn Efficient Inter-Carrier Interference Cancellation Scheme for OFDM Systems\u201d, International Journal of Computer Science and Information Security, IJCSIS, Vol. 6, No. 3, pp. 141-148, December 2009, USA.<\/p>\n[3] Thamer M. Jamel, Sura H. Khadum, Hassan F. Khazaal, \u201cComparative Analysis of Orthogonal Time Frequency Space in Mobile Communications: A Review and Simulation Study\u201d, IEEE 22nd International Multi-Conference on Systems, Signals & Devices (SSD), 2025.<\/p>\n[4] R. Hadani et al., “Orthogonal Time Frequency Space Modulation,” IEEE WCNC, 2017.<\/p>\n[5] Simone Servadio, Renato Zanetti, and Roberto Armellin, \u201cMAXIMUM A POSTERIORI ESTIMATION OF HAMILTONIAN SYSTEMS WITH HIGH ORDER SERIES EXPANSIONS\u201d, The University of Texas at Austin, 2024.<\/p>\n[6] Manoj Kumar S;\u00a0Shyama Sreeekumar;\u00a0Sameer S. M., \u201cJoint CFO and Channel Estimation for OTFS-Based High Mobility Wireless Communication Systems\u201d, 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT).<\/p>\n[7] Juan Meng,\u00a0Ziping Wei,\u00a0Yang Zhang,\u00a0Bin Li\u00a0&\u00a0Chenglin Zhao, \u201cMachine learning based low-complexity channel state information estimation\u201d, EURASIP J. Adv. Signal Process.\u00a02023, 98 (2023). https:\/\/doi.org\/10.1186\/s13634-023-00994-4.<\/p>\n<\/div><\/div>\n

\n

Session 2: Artificial Intelligence and Medical Diagnosis<\/strong><\/p>\n

Monday, <\/strong>December 15, 2025, 14h30 \u2013 16h00<\/p>\n<\/div><\/div>\n

\n

Paper #: S2-KSp3A<\/em><\/p>\n

Smart Humanoid Robots in <\/strong>Paediatric<\/strong> Diabetes Management<\/strong><\/h2>\n

Majid Al-Taee1,*<\/sup>, Zahra J. Muhsin2<\/sup>, Ahmad Al-Taee3<\/sup><\/p>\n

1<\/sup><\/em>Consultant of Computing and Intelligent Systems Engineering, Liverpool, United Kingdom<\/em><\/p>\n

2<\/sup><\/em>School of Computing, Arden University, Manchester, United Kingdom<\/em><\/p>\n

3<\/sup><\/em>Loyola University Medical Center, Maywood, IL 60153, United States<\/em><\/p>\n

*Contact: altaeema@gmail.com<\/em><\/p>\n

\u00a0<\/u><\/em><\/p>\n

Abstract: <\/strong><\/p>\n

Smart robotics is reshaping the landscape of diabetes care in children and adolescents by enhancing accuracy, personalization, and the ability to continuously monitor health even at a distance. Unlike traditional care models that often depend on periodic clinic visits, IoT-enabled eHealth platform that integrates humanoid robots to support pediatric diabetes self-management enables real-time data collection and feedback, ensuring timely interventions. Social and virtual robotic assistants provide crucial support in managing daily routines, helping young patients and their families track diet, exercise, and medication adherence in engaging and interactive ways. This study proposes an eHealth platform that leverages IoT connectivity, robotic coaching, and artificial intelligence (AI) to enhance diabetes care beyond hospital settings into the home environment. The humanoid robot wirelessly collects biometric measurements from patient devices, including glucose monitors and activity trackers, while also capturing data on diet, insulin intake, and emotional well-being through conversational interactions. These data streams are transmitted to a centralized smart disease management hub, where AI algorithms analyze the information and generate tailored recommendations. Feedback is then relayed to patients through the robot, offering personalized guidance in a supportive and interactive manner. A pilot clinical study of the platform demonstrated strong promise: patient satisfaction exceeded 86%, and more than 90% of participants expressed a positive perception of the robot as an innovative medical device. These findings highlight the transformative potential of robotics in pediatric diabetes care, pointing to a future where technology not only empowers children and families to actively manage T1DM with confidence but also improves clinical outcomes.<\/p>\n

Keywords<\/strong>: AI-enabled telecare, eHealth, human-robot interaction, Internet of things (IoT), Pediatric diabetes management, Remote patient monitoring.<\/p>\n

References<\/strong><\/p>\n[1] \u00a0M. Al-Taee, Zahra J. Muhsin, Waleed Al-Nuaimy and Ahmad Al-Taee. “Smart Care: An IoT Platform with Humanoid Robots for Diabetes Management in Children.” in IEEE 22nd<\/sup> Int. Multi-Conf. on Systems, Signals & Devices (SSD), Tunisia, 17-20 February 2025, pp. 494-499.<\/p>\n[2]\u00a0\u00a0\u00a0 M. Smudja, T. Milenkovi\u0107, I. Minakovi\u0107, V. Zdravkovi\u0107, J. Javorac, and D. Milutinovi\u0107, “Self-care activities in pediatric patients with type 1 diabetes mellitus,” Plos one, vol. 19(3): e0300055, 2024.<\/p>\n[3] \u00a0M. A. Al-Taee, S. N. Abood, W. Al-Nuaimy and A. M. Al-Taee, “Blood-glucose pattern mining algorithm for decision support in diabetes management,”\u00a0in 2014 14th UK Workshop on Computational Intelligence (UKCI), Bradford, UK, 2014, pp. 1-7.<\/p>\n[4]\u00a0\u00a0\u00a0 A. M. Al-Taee, M. A. Al-Taee, W. Al-Nuaimy, Z. J. Muhsin, and H. AlZu’bi, “Smart bolus estimation taking into account the amount of insulin on board,” in IEEE Conf. on Computer and Info Technology and Pervasive Intelligence and Computing, Liverpool, UK, 2015, pp. 1051-1056.<\/p>\n[5]\u00a0\u00a0\u00a0 S. C. Mackenzie, C. A. Sainsbury, and D. J. Wake, “Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges,” Diabetologia, vol. 67(2): 223-235, 2024.<\/p>\n[6]\u00a0\u00a0\u00a0 M. A. Al-Taee and S. N. Abood, “Mobile acquisition and monitoring system for improved diabetes management using emergent wireless and web technologies,” Int. Journal of Information Technology and Web Engineering, vol. 7(1): 17-30, 2012.<\/p>\n[7]\u00a0\u00a0\u00a0 T. Alhmiedat, L. A. AlBishi, F. Alnajjar, M. Alotaibi, A. M. Marei, and R. Shalayl, “Improving diabetes education and metabolic control in children using social robots: A randomized trial,” Technologies, vol. 12(11): 209, 2024.<\/p>\n[8]\u00a0\u00a0\u00a0 A. M. Al-Taee, A. Al-Taee, Z. Muhsin, M. A. Al-Taee, and W. Al-Nuaimy, “Towards developing online compliance index for self-monitoring of blood glucose in diabetes management,” in 2016 Int. Conf. on Developments in eSystems Engineering (DeSE), Liverpool & Leeds, UK, 2016, pp. 45-50.<\/p>\n[9]\u00a0\u00a0\u00a0 M. A. Al-Taee, W. Al-Nuaimy, Z. J. Muhsin, and A. Al-Ataby, “Robot assistant in management of diabetes in children based on the Internet of things,” IEEE Internet of Things Journal, vol. 4(2): 437-445, 2016.<\/p>\n<\/div><\/div>\n

\n

Paper #: S2-KSp3B<\/em><\/p>\n

AI-Driven Rapid-Acting Insulin Bolus Adjustment Using Insulin on Board for Type 1 Diabetes Management<\/strong><\/h2>\n

Majid Al-Taee1,*<\/sup>, Zahra J. Muhsin2<\/sup>, Anas Al-Taee3<\/sup><\/p>\n

1<\/sup><\/em>Consultant of Computing and Intelligent Systems Engineering, Liverpool, United Kingdom<\/em><\/p>\n

2<\/sup><\/em>School of Computing, Arden University, Manchester, United Kingdom<\/em><\/p>\n

3<\/sup><\/em>Pines Health Services and Cary Medical Centre, Caribou, ME 04736, United States <\/em><\/p>\n

*Contact: altaeema@gmail.com <\/em><\/p>\n

\u00a0<\/u><\/p>\n

Abstract: <\/strong><\/p>\n

Accurate calculation of rapid-acting insulin boluses is a cornerstone of effective blood glucose self-management for individuals with type 1 diabetes. Miscalculations can lead to hyperglycemia from under-dosing or hypoglycemia from over-dosing, both of which pose serious health risks. Thus, reliable tools that assist patients in precise dosing are essential. This study proposes a machine learning\u2013based insulin bolus adjustment model, designed to underpin an AI-driven decision support system that provides real-time, individualized dosage recommendations. Unlike conventional bolus calculators that rely solely on carbohydrate intake and correction factors, the proposed system incorporates an additional dynamic variable to enhance accuracy and safety. A key contribution of this approach lies in its ability to estimate the residual insulin still active in the patient\u2019s body, commonly referred to as \u201cinsulin on board\u201d (IOB). This estimation is performed using an artificial neural network, which processes inputs such as the time elapsed since the last rapid-acting insulin injection and the pharmacological properties of the specific insulin formulation administered. By quantifying the IOB, the system adjusts the recommended dose to reduce the likelihood of insulin stacking and subsequent hypoglycemia. The final bolus recommendation integrates three main components: insulin required for carbohydrate coverage, a correction dose to address deviations from target glucose levels, and adjustments for contextual factors such as recent physical activity, stress, illness, alcohol consumption, or the macronutrient composition of the meal. The inclusion of IOB as an explicit adjustment further refines the accuracy of the dosage calculation. To implement this model, a fully functional application was developed and tested. Preliminary results indicate that the tool provides accurate, personalized insulin recommendations, supporting safer glycemic control and enhancing the quality of life for individuals with type 1 diabetes.<\/p>\n

Keywords<\/strong>: Artificial intelligence, Artificial neural networks, Rapid-acting insulin dosing, Decision support systems, Glycemic control, Type 1 diabetes.<\/p>\n

References:<\/strong><\/p>\n[1] \u00a0Z. J. Muhsin, R. Qahwaji, M. AlShawabkeh, M. Al Bdour, S. AlRyalat and M. Al-Taee, \u201cTwo-stage ensemble learning frameworkfor automated classification of keratoconus severity,\u201d Computers in Biology and Medicine Journal, vol. 195:110568,2025.<\/p>\n[2] \u00a0Z. J. Muhsin, R. Qahwaji, M. AlShawabkeh, M. Al Bdour, S. AlRyalatand M. Al-Taee, \u201cHighly efficient stacking ensemble learningmodel for automated keratoconus screening,\u201d Eye and Vision Journal, vol. 12(1):1-20,2025.<\/p>\n[3] \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. AlShawabkeh, M. Al Bdour, S. AlRyalat and M. Al-Taee, “Advances in machine learningfor keratoconus diagnosis,” International Ophthalmology Journal, vol. 45(128):1-22, 2025.<\/p>\n[4] \u00a0Z. J. Muhsin, R. Qahwaji, M. AlShawabkeh, S. A. AlRyalat, M. Al Bdour and M. Al-Taee, “Smart decision support system forkeratoconus severity staging using corneal curvature and thinnest pachymetry indices,” Eye and Vision, vol. 11(1): 1-20, 2024.<\/p>\n[5] \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. Al Bdour, S. AlRyalat, M. AlShawabkeh and M. Al-Taee, “Clinician-assisted exploratorydata analysis framework for early diagnosis of keratoconus, ” in 2025 IEEE 22ndInternational Multi-Conference on Systems, Signals& Devices (SSD), Monastir, Tunisia, 17-20 February 2025, pp. 215-220.<\/p>\n[6] \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. Al Bdour, S. AlRyalat, M. AlShawabkeh and M. Al-Taee, “Keratoconus severity staging using random forest and gradient boosting ensemble techniques,” in IEEE 22ndInternational Multi-Conference on Systems, Signals & Devices (SSD), Monastir, Tunisia, 17-20 February 2025,pp. 593-598.<\/p>\n[7] Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. Al Bdour, S. AlRyalat, M. AlShawabkeh and M. Al-Taee, “Performance comparison of machine learning algorithms for keratoconus detection,” in IEEE 30th<\/sup> Int. Conf. on Telecoms (ICT\u201924), Amman, Jordan, 24-27 June 2024, pp. 01-06.<\/p>\n[8] B. Al-Bander, B. Williams, M. Al-Taee, W. Al-Nuaimy and Y. Zheng, \u201dA novel choroid segmentation method for retinal diagnosisusing deep learning,\u201d in 2017 Int. Conf. on Developments in eSystems Engineering (DeSE\u20192017), Paris, France,14-16 June 2017, pp. 182-187.<\/p>\n<\/div><\/div>\n

\n

Paper #: S2-1<\/em><\/p>\n

Molecular Detection of Mutations in BRCA1 gene (exon 11) in Iraqi Breast Cancer Patients and their Families<\/strong><\/h2>\n

\u00a0<\/strong><\/p>\n

Shatha S. Jumaah1*<\/sup> and Khalid K. Tobal2<\/sup><\/p>\n

1<\/sup><\/em> Biology Dept., Education Faculty, University of Al-Hamdaniya Iraq<\/em><\/p>\n

2<\/sup><\/em>Oncology Dept., Guy\u2019s& St. Thomas\u2019s Hospital, UK<\/em><\/p>\n

*Contact: drshathasaadi@uohamdaniya.edu.iq <\/a>Phone: +964 7731329529<\/em><\/p>\n

\u00a0<\/strong><\/p>\n

Abstract: <\/strong><\/p>\n

Detecting gene mutations for cancer patients is very important for finding the genes which can affect the susceptibility and\/or prognosis. BRCA1 and BRCA2 recorded and considered as the most common cause of hereditary breast cancer. The whole exon 11 was covered for thirty-six blood samples of Iraqi breast cancer patients by dividing into six amplicons.\u00a0 Also, blood samples from 1 to 2 family members for each patient were obtained; exactly 1st<\/sup> and 2nd<\/sup> degree relationship and ten blood samples of apparently healthy control were collected. Conventional Sanger sequencing was used to detect the BRCA1 mutations in their specific amplicons by using Mutation Surveyor Release version 3.24 copyright 2006 Software to screen for mutations. BRCA1 mutations recorded in 30 cases and all detected amplicons mutations were point mutation (missense type). The detected point mutations in blood samples included (a.2128A>G, T710A), (c. 2271C>T, P724L), (t. 1949T>G, I650R), (t. 2255T>C, L752S), (a. 2938A>C, I980L) and (t.4032T>A, D1344E), none of the relatives included in the study showed similar mutations except in (t. 2255T>C, L752S) appeared in first degree relative, and statistical analysis was significant (P<0.01). As a conclusion, it is possible to pass the deleterious mutations through generations. The detected polymorphisms are very important in early detection of breast cancer, and this confirmed by sequencing results.<\/p>\n

Keywords:<\/strong> Breast Cancer, Mutation, High-Resolution Melt (HRM), BRCA1, Exon 11.<\/p>\n

References:<\/strong><\/p>\n[1]\u00a0 A. N. AL-Thaweni, W. H. Yousif, and S. S. Hassan, \u201cDetection of BRCA1and BRCA2 mutation for Breast Cancer in Sample ofIraqi Women above 40 Years,\u201dBaghdad Science Journal, vol. 7, no. 1, pp. 394\u2013400, Mar. 2025, doi:https:\/\/doi.org\/10.21123\/bsj.2010.7.1.394-400.<\/p>\n[2] N. A. S. Alwan, \u201cBreast Cancer Among Iraqi Women: Preliminary Findings From a Regional Comparative Breast Cancer ResearchProject,\u201dJournal of Global Oncology, vol. 2, no. 5, pp. 255\u2013258, Oct. 2016, doi:https:\/\/doi.org\/10.1200\/jgo.2015.003087.<\/a><\/p>\n[3] B. M. Crossleyet al., \u201cGuidelines for Sanger sequencing and molecular assay monitoring,\u201dJournal of Veterinary DiagnosticInvestigation, vol. 32, no. 6, pp. 767\u2013775, Feb. 2020, doi:https:\/\/doi.org\/10.1177\/104063<\/a>8720905833.<\/p>\n[4]\u00a0 X. Fu, W. Tan, Q. Song, H. Pei, and J. Li, \u201cBRCA1 and Breast Cancer: Molecular Mechanisms and Therapeutic Strategies,\u201dFrontiersin Cell and Developmental Biology, vol. 10, Mar. 2022, doi:https:\/\/doi.org\/10.3389\/fcell.2022.813457.<\/a><\/p>\n[5] H. T. Hashim, M. A. Ramadhan, K. M. Theban, J. Bchara, A. El-Abed-El-Rassoul, and J. Shah, \u201cAssessment of breast cancer riskamong Iraqi women in 2019,\u201dBMC Women\u2019s Health, vol. 21, no. 1, Dec. 2021, doi:https:\/\/doi.org\/10.1186\/s12905<\/a>-021-01557-1.<\/p>\n[6]\u00a0 E. M. Rosen, S. Fan, R. G. Pestell, and I. D. Goldberg, \u201cBRCA1 gene in breast cancer,\u201dJournal of Cellular Physiology, vol. 196,no. 1, pp. 19\u201341, May 2003, doi:https:\/\/doi.org\/10.1002\/jcp.10257.<\/a><\/p>\n[7] F. Sanger, S. Nicklen, and A. R. Coulson, \u201cDNA sequencing with chain-terminating inhibitors,\u201dProceedings of the NationalAcademy of Sciences, vol. 74, no. 12, pp. 5463\u20135467, Dec. 1977<\/p>\n<\/div><\/div>\n

\n

Paper #: S2-2<\/em><\/p>\n

Highly Accurate Super Learner for Tomographic and Clinical <\/strong>Keratoconus Classification<\/strong><\/h2>\n

Zahra J. Muhsin1,*, Rami Qahwaji1, Ibrahim Ghafir1, Saif AlRyalat2, Muawyah Al Bdour2, Mo\u2019ath AlShawabkeh3, Majid Al-Taee4<\/p>\n

1Faculty of Eng. and Digital Technologies, University of Bradford, UK<\/em><\/p>\n

2Scool of Medicine, The University of Jordan; Amman, Jordan<\/em><\/p>\n

3Faculty of Medicine, The Hashemite University, Zarqa, Jordan<\/em><\/p>\n

4Consultant of Computing and Intelligent Systems Engineering, Liverpool, UK<\/em><\/p>\n

*Contact: z.j.muhsin@bradford.ac.uk<\/em><\/p>\n

\u00a0<\/em><\/p>\n

Abstract: <\/strong><\/p>\n

Early diagnosis of keratoconus (KC) is difficult due to normal slit-lamp exams and minimal visual acuity impact. Despite proposing many machine learning models, clinical adoption is limited by insufficient AI-clinical collaboration. This study proposes a super learner, jointly developed with clinicians, based on corneal tomography to more accurately differentiate Tomographic KC (TKC) from Clinical KC (CKC). This study utilized 79 Pentacam-derived features from 640 corneas (300 TKC, 340 CKC). To balance the dataset and avoid bias, an oversampling method was applied, ensuring 340 samples for each class. After pre-processing, the feature set was reduced by 26.58%. Statistical analysis based on relative importance scores and feature dependency, with input from clinicians, further reduced features to 3.78%. The selected features focus on corneal curvature radii and thinnest pachymetry, excluding visual acuity test. A super learner, combining the complementary strengths of top performing models was then built by stacking Random Forest, Gradient Boost, and Decision Tree models, with their predictions used as input for a Support Vector Machine meta-classifier for final predictions. Performance of the developed model was evaluated both through standard ML metrics, as well as by expert assessment from ophthalmologists. It achieved 99.26% accuracy, 99.27% precision, and 99.26% sensitivity. F1 and F2 scores were 99.42% and 99.41%, respectively. It perfectly classified all CKC test cases and misclassified a single borderline TKC case out of 68. These results show robust and near-perfect performance in distinguishing TKC from CKC, demonstrating strong promise in aiding KC diagnosis with objective, consistent criteria, reducing human error. It simplifies the diagnosis process by relying solely on few key Pentacam indices. Clinician involvement throughout development ensures clinical relevance, enhancing its potential for adoption in routine practice.<\/p>\n

Keywords:<\/strong> Corneal Tomography, Classification, Ensemble Learning, Keratoconus diagnosis, Ophthalmology.<\/p>\n

References:<\/strong><\/p>\n[1]\u00a0 \u00a0Z. J. Muhsin, R. Qahwaji, M. AlShawabkeh, M. Al Bdour, S. AlRyalat and M. Al-Taee, \u201cTwo-stage ensemble learning frameworkfor automated classification of keratoconus severity,\u201d Computers in Biology and Medicine Journal, vol. 195: 110568, 2025.<\/p>\n[2]\u00a0 Z. J. Muhsin, R. Qahwaji, M. AlShawabkeh, M. Al Bdour, S. AlRyalat and M. Al-Taee, \u201cHighly efficient stacking ensemblelearningmodel for automated keratoconus screening,\u201d Eye and Vision Journal, vol. 12(1): 1-20, 2025.<\/p>\n[3]\u00a0 \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. AlShawabkeh, M. Al Bdour, S. AlRyalat and M. Al-Taee, “Advances in machine learningfor keratoconus diagnosis,” International Ophthalmology Journal, vol. 45(128): 1-22, 2025.<\/p>\n[4] \u00a0Z. J. Muhsin, R. Qahwaji, M. AlShawabkeh, S. A. AlRyalat, M. Al Bdour and M. Al-Taee, “Smart decision support system forkeratoconus severity staging using corneal curvature and thinnest pachymetry indices,” Eye and Vision, vol. 11(1): 1-20, 2024.<\/p>\n[5] \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. Al Bdour, S. AlRyalat, M. AlShawabkeh and M. Al-Taee, “Clinician-assisted exploratorydata analysis framework for early diagnosis of keratoconus,” in 2025 IEEE 22ndInternational Multi-Conference on Systems, Signals& Devices (SSD), Monastir, Tunisia, 17-20 February 2025, pp. 215-220.<\/p>\n[6] \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. Al Bdour, S. AlRyalat, M. AlShawabkeh and M. Al-Taee, “Keratoconus severity stagingusing random forest and gradient boosting ensemble techniques,” in 2025 IEEE 22ndInternational Multi-Conference on Systems,Signals & Devices (SSD), Monastir, Tunisia, 17-20 February 2025, pp. 593-598.<\/p>\n[7] \u00a0Z. J. Muhsin, R. Qahwaji, I. Ghafir, M. Al Bdour, S. AlRyalat, M. AlShawabkeh and M. Al-Taee, “Performance comparison ofmachine learning algorithms for keratoconus detection,” in IEEE 30th<\/sup> Int. Conf. on Telecoms (ICT\u201924), Amman,Jordan, 24-27 June 2024, pp. 01-06.<\/p>\n[8] B. Al-Bander, B. Williams, M. Al-Taee, W. Al-Nuaimy and Y. Zheng, \u201dA novel choroid segmentation method for retinal diagnosisusing deep learning,\u201d in 2017 Int. Conf. on Developments in eSystems Engineering (DeSE\u20192017), Paris, France, 14-16 June 2017,pp. 182-187.<\/p>\n<\/div><\/div>\n

\n

Paper #: S2-3<\/em><\/p>\n

Bibliometric Analysis of the Correlation Between Deep Learning and Alzheimer\u2019s Disease Detection<\/strong><\/h2>\n

\u00a0<\/strong><\/p>\n

Raya Kh. Yashooa1<\/sup>*, Abd Al-Bar Al-Farha2<\/sup>, <\/sub>Wissam Albeer Nooh1<\/sup>, shatha S. Jumaah1<\/sup>, Ali Q. Saeed3<\/sup>, <\/sub>Ahmad Samed Al-Adwan4<\/sup>, Ahmad Alshamayleh5<\/sup>, Taher M. Ghazal6,7<\/sup><\/p>\n

1<\/sup><\/em>Department of Biology, University of Al-Hamdaniya, Mosul, 41002, Iraq.<\/em><\/p>\n

2<\/sup><\/em>Center of Technical Research, Northern Technical University, Mosul, 41002, Iraq.<\/em><\/p>\n

3<\/sup><\/em>Scientific Affairs division, Northern Technical University, Mosul, Ninevah,41002, Iraq.<\/em><\/p>\n

4<\/sup><\/em>Business Technology Department, Al-Ahliyya Amman University, Amman, Jordan<\/em><\/p>\n

5<\/sup><\/em>Department of Data Science and AI, Al-Ahliyya Amman University, Amman, Jordan.<\/em><\/p>\n

6<\/sup><\/em>Department of Networks and Cybersecurity, Al-Ahliyya Amman University, Amman, Jordan.<\/em><\/p>\n

7<\/sup><\/em>Center for Cyber Security, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Selangor, Malaysia.<\/em><\/p>\n

*Contact: Raya Kh. Yashooa raya.yashooa@uohamdaniya.edu.iq, Phone:009647711684077<\/em><\/p>\n

\u00a0<\/em><\/strong><\/p>\n

Abstract:<\/strong><\/p>\n

This study focuses on the bibliometric examination of studies on Alzheimer’s disease, AD, and deep learning. The Web of Science Core Collection data were obtained using specific search phrases related to deep learning and Alzheimer’s from the Science Citation Index (SCI-E). The collection comprises of English-language papers from computer science that were generated in recent years. Each publication was meticulously examined to exclude all extraneous content, including books, retracted papers, and uses of artificial intelligence (AI) beyond finance. Any discrepancies that arose during the selection process were resolved through group deliberation. A thorough bibliometric analysis utilized three instruments: Bibliometric.com, VOSviewer, and Publish or Perish. Seventy-nine empirical research articles published in various nations from 2018 to 2024 were selected for this approach. The quantity of pertinent articles has risen annually. In 2022, China led in article production with n=27 (28.7%), followed closely by the USA with n=22 (23.4%) and the UK with n=11 (11.7%). Viewed from this perspective, the three countries accounted for 63.8% of the output. The five leading journals discovered were IEEE Access, which published five publications (17.2%), and IEEE Transactions on Medical Imaging, which published four articles (13.7%); both are in the USA. This indicates that only the institutions of the University of Pennsylvania, the University of Southern California, and the Chinese Academy of Sciences published more than one article in this study. The primary areas of interest discovered by keyword analysis for study focus include ‘detection,’ ‘diagnostic,’ ‘imaging,’ ‘Alzheimer’s,’ and ‘formula.’ The findings emphasize the transformation and advancement that deep learning has introduced in diagnosing and treating Alzheimer’s disease. Subsequent research should concentrate on equitable multinational collaborations that include AI to enhance physicians’ practices, improving the quality of care.<\/p>\n

Keywords<\/strong>: Deep learning, Artificial intelligent, Alzheimer disease, detection, Bibliometric analysis, VOSviewer.<\/p>\n

References:<\/strong><\/p>\n[1] R. A. Sperling et al., “Toward defining the preclinical stages of Alzheimer\u2019s disease: Recommendations from the National InstituteonAging-Alzheimer’s Association workgroups, ” Alzheimer’s & dementia, vol. 7,no. 3, pp. 280-292, 2011.<\/p>\n[2] A. Atri, “The Alzheimer\u2019s disease clinical spectrum: diagnosis and management,” Medical Clinics, vol.103, no. 2, pp. 263-293,2019.<\/p>\n[3] H. Matsuda, Y. Shigemoto, and N. Sato, “Neuroimaging of Alzheimer\u2019s disease: focus on amyloid and tau PET,” Japanese journalof radiology, vol. 37, pp. 735-749, 2019.<\/p>\n[4] R. K. Yashooa and A. Q. Nabi, “The miR-146a-5p andmiR-125b-5p levels as biomarkers for early prediction of Alzheimer’sdisease,” Human Gene, vol. 34, p. 201129, 2022.<\/p>\n[5] R. K. Yashooa and A. Q. Nabi, “Telomerase Reverse Transcriptase and Telomerase RNA Component Gene Expression as a NovelBiomarkers forAlzheimer\u2019s disease,” Cellular and Molecular Biology, vol. 68, no. 9, pp. 14-20, 2022.<\/p>\n[6] L. E. Hebert, J. Weuve, P. A. Scherr, and D. A. Evans, “Alzheimer disease in the United States (2010\u20132050) estimated using the2010 census,” Neurology, vol. 80, no.19, pp. 1778-1783, 2013.<\/p>\n[7] A. Association, “Alzheimer\u2019s disease facts and figures,” Alzheimer\u2019s Dement, vol. 15, no. 3, pp. 321-387, 2019.<\/p>\n<\/div><\/div>\n

\n

Paper #: S2-4<\/em><\/p>\n

Deep Learning and Hybrid Techniques Evaluation for Enhancing Diagnostic Performance of Bladder Cancer<\/strong><\/h2>\n

Safa Anmar Albarwary1*<\/sup>, and Berna Uzun 2,3<\/sup><\/p>\n

1<\/sup>Mechatronics Engineering Department, Tishk International University, Erbil, Iraq<\/p>\n

2<\/sup>Operation Research Center in Healthcare, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus<\/p>\n

3<\/sup>Department of Mathematics, Faculty of Letters and Sciences, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus<\/p>\n

*Contact: safa.anmar@tiu.edu.iq, phone: +9647502078406<\/p>\n

\u00a0<\/u><\/p>\n

Abstract:<\/strong><\/p>\n

Most urological malignancies include Bladder Cancer (Bla.C.) initial examination and post-treatment monitoring require a precise and efficient diagnosis. Clinicians are interested in new diagnostic tools to fill this gap. Recent research shows that artificial intelligence (AI) can improve bladder cancer diagnosis using Deep learning (DL) or hybrid models (Machine Learning with Deep Learning models). Data-driven methods can analyze complex medical imaging and clinical data, reduce diagnosis errors and help clinicians. To find the optimal diagnostic strategy, comparing models across datasets and using numerous evaluation criteria is required. This study uses the multi-criteria decision-making (MCDM) approach to analyze AI-based models for bladder cancer diagnosis. The fuzzy Preference Ranking Organization Method for Enrichment Evaluation (F-PROMETHEE) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analytical approaches are used to rank the alternative deep learning models based on performance metrics and expert judgment. For this investigation, the most sophisticated deep learning and hybrid models reported between 2020 and 2025 examined more than 15 models for accuracy, sensitivity, specificity, precision, and F1 score. This study provides a comprehensive comparative analysis of cutting-edge AI models for bladder cancer diagnosis, using extensive performance indicators and sophisticated decision-making methodologies.\u00a0 Transformer-based designs, have emerged as the leading models, demonstrating superior accuracy, sensitivity, precision, and F1-score, highlighting their capacity to detect nuanced patterns in histopathology images and reduce diagnostic omissions. Utilizing TOPSIS and fuzzy PROMETHEE to integrate multi-criteria data and manage uncertainty in imputed values, we attained a sophisticated, clinically relevant ranking of models that can guide more assured, context-specific judgments on AI use in bladder cancer diagnosis.<\/p>\n

Keywords<\/strong>: Bladder Cancer, Deep Learning (DL), Vision Transformers (ViTs), MCDM, Fuzzy PROMETHEE II, TOPSIS.<\/p>\n

References:<\/strong><\/p>\n[1]L. Cai et al., \u201cDeep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study,\u201d Scientific Reports, vol. 15, no. 1, Mar. 2025, doi: 10.1038\/s41598-024-82909-3.<\/p>\n[2]W. K. Hwang et al., \u201cArtificial Intelligence-Based Classification and Segmentation of bladder cancer in cystoscope images, \u201dCancers, vol. 17, no. 1, p. 57, Dec. 2024, doi: 10.3390\/cancers17010057.<\/p>\n[3]Y. Mehmood and U. I. Bajwa, \u201cBrain tumor grade classification using the ConvNext architecture,\u201d Digital Health, vol. 10, Jan.2024, doi: 10.1177\/20552076241284920.<\/p>\n[4]D. I. Emegano, M. T. Mustapha, I. Ozsahin, D. U. Ozsahin, and B. Uzun, \u201cAdvancing Prostate Cancer Diagnostics: AConVNEXT Approach to Multi-Class Classification in Underrepresented Populations,\u201d Bioengineering, vol. 12, no. 4, p. 369,Apr. 2025, doi: 10.3390\/bioengineering12040369.<\/p>\n[5]B. Ozdemir and I. Pacal, \u201cA robust deep learning framework for multiclass skin cancer classification,\u201d Scientific Reports, vol.15, no. 1, Feb. 2025, doi: 10.1038\/s41598-025-89230-7.<\/p>\n[6]N. Drissi, H. El-Kassabi, and M. A. Serhani, \u201cA multi-criteria decision analysis framework for evaluating deep learning models in healthcare research,\u201d Decision Analytics Journal, vol. 13, p. 100523, Oct. 2024, doi: 10.1016\/j.dajour.2024.100523.<\/p>\n[7]H. Erdagli, D. U. Ozsahin, and B. Uzun, \u201cEvaluation of myocardial perfusion imaging techniques and artificial intelligence (AI)tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method,\u201d Cardiovascular Diagnosis andTherapy, vol. 14, no. 6, pp. 1134\u20131147, Dec. 2024, doi: 10.21037\/cdt-24-237.<\/p>\n<\/div><\/div>\n

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Session 3 [online]: Healthcare Applications and Networks<\/strong><\/p>\n

Monday, <\/strong>December 15, 2025, 16h00 \u2013 17h15<\/p>\n<\/div><\/div>\n

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Paper #: S3-1<\/em><\/p>\n

Launching and Strategic Implementation of the First Digital <\/strong>Bank in Iraq<\/strong><\/h2>\n

\u00a0<\/strong><\/p>\n

Nabeel Raheem alebady<\/p>\n

Chairman of the Founding Committee of the Iraqi Digital Bank, Iraq<\/p>\n

Email: Nabeel.Alebady@DIB-iq.com<\/p>\n

Abstract:<\/strong><\/p>\n

This presentation will provide an in-depth analysis of the Iraqi Digital Bank’s groundbreaking journey to redefine traditional banking. We will examine its core strategy of integrating Digital Technology and AI to create a seamless, secure, and customer-centric banking ecosystem. The discussion will center on how this transformation:<\/p>\n