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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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Binary to Decimal Conversion and Subnetting Calculations using Augmented Reality 9

Binary to Decimal Conversion and Subnetting Calculations usi...

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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Ramdania, Diena Rauda Alam, Cecep Nurul Ghuswari, Afif Hani Irfan, Mohamad Atmadja, Aldy Rialdy Harika, Maisevli UIN Sunan Gunung Djati Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (纸本)9798350370126

This paper studies markerless augmented reality applications for displaying digital objects on different colored surfaces under varying lighting conditions and tracking angles. The study aimed to evaluate the application's effectiveness in accurately displaying digital objects. The testing was conducted in bright and dark light settings, with tracking angles of 45° and 90°. The results showed that surfaces with a texture in dark or bright colors, especially patterned surfaces, yielded optimal results in displaying digital objects in both lighting conditions. However, plain bright surfaces and surfaces without patterns were not practical for displaying digital objects. The angle of the tracking device and the presence of shadows significantly impacted the quality of the displayed objects. The tracking distance did not significantly affect the display quality. These findings provide valuable insights for enhancing the performance and usability of markerless augmented reality applications, guiding the selection of suitable surfaces and lighting conditions for optimal display outcomes. © 2023 IEEE.

关键词： Augmented reality

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Assessing students' awareness and education of web accessibility in web development and interface design programs 10

Assessing students' awareness and education of web accessibi...

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10th International Conference on Socio-Technical Perspectives in Information Systems, STPIS 2024

作者： Henriksson, Elin Uddstrom, Anna van Bree, Lotte Department of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden

In an era of growing digitization, technology is essential for communication and daily life. However, inaccessible websites, including in Sweden, create barriers for individuals with disabilities, often due to insufficient education and awareness among web developers. Our quantitative study aimed to assess the status of web accessibility education within Swedish web development and interface design programs. We analyzed curricula and surveyed 63 final-year students across 23 programs. Findings show that while most programs (18 out of 23) include web accessibility, integration levels vary. Only 2% of courses offered standalone accessibility objectives, and 7% incorporated it within other courses, leaving 91% without any. Student knowledge varied;69.8% were aware of accessibility guidelines and laws, but 30.2% were not. Additionally, only 26.2% of students reported acquiring knowledge about accessibility guidelines and laws from their current education. This underscores the need for better web accessibility education. © 2024 Copyright for this paper by its authors.

关键词： Accessibility accessibility guidelines education interface design WCAG web accessibility web development

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Cyber Threat Intelligence in Smart Cities: Bayesian Inference and Energy Optimization in LoRa Networks for Big Data Applications

Cyber Threat Intelligence in Smart Cities: Bayesian Inferenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Karras, Aristeidis Theodorakopoulos, Leonidas Karras, Christos Antonopoulou, Hera University of Patras Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece

ISBN: (纸本)9798350362480

In the evolving landscape of smart cities, optimizing energy consumption and enhancing cybersecurity in Internet of Things (IoT) networks are crucial. This study leverages LoRa (Long Range) technology, Bayesian Inference, and Extreme Learning Machines (ELMs) to advance cyber threat intelligence and energy efficiency in large-scale IoT deployments. The proposed algorithms address key challenges within LoRa networks by implementing a novel energy consumption model, kernel-based ELM fine-tuning, Bayesian parameter tuning, and data fusion. Through Bayesian Inference, our approach dynamically adjusts network parameters to optimize packet transmission and collision rates, ultimately reducing power consumption across smart city applications. The Kernel-ELM algorithm fine-tunes LoRa network applications by adapting kernel parameters to the unique demands of IoT environments. Additionally, our Anomaly Detection and Fusion Algorithm (ADFA) integrates data from multiple sources to detect potential cyber threats, enhancing network security. Experimental results validate these algorithms on various datasets, demonstrating improvements in both energy optimization and threat detection. Ultimately, this study provides actionable insights into the deployment of scalable, energy-efficient, and secure IoT networks for smart city infrastructure. © 2024 IEEE.

关键词： Cyber attacks

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Performance Evaluation of Wearable Internet of Things Devices for Indoor Positioning 10

Performance Evaluation of Wearable Internet of Things Device...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Papadopoulos, George Spachos, Petros University of Thessaly Department of Computer Science and Biomedical Informatics Greece University of Guelph School of Engineering GuelphON Canada

ISBN: (纸本)9798350373011

Indoor localization and tracking services are necessary for several popular applications from asset monitoring to location-based marketing. The use of Internet of Things (IoT) devices in those services has increased due to their low cost and ease of deployment. However, indoor services pose significant challenges due to the lack of GPS signals as well as the complexity of the indoor environment. Alternative approaches, mainly including wireless technologies, should be considered. In this work, we examine the feasibility of using wearable IoT devices for indoor tracking. Specifically, we use smartwatches and we examine the performance of Wi-Fi and Bluetooth Low Energy (BLE) for indoor tracking. According to experimental results, BLE outperformed Wi-Fi in terms of energy efficiency while Wi-Fi, on the other hand, demonstrated a slightly better accuracy in Line of Sight conditions. © 2024 IEEE.

关键词： Bluetooth

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Assisting Neurosurgeons: Advancing Brain Tumor Surgery with Smartphone Augmented Reality Navigation

Assisting Neurosurgeons: Advancing Brain Tumor Surgery with ...

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2024 International Conference on Innovation and Intelligence for informatics, Computing, and Technologies, 3ICT 2024

作者： Java, Muhammad Iskandar Hidayati, Shintami Chusnul Sarno, Riyanarto Anistyasari, Yeni Ali, Khurshed Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9798331533137

Integrating Augmented Reality (AR) into neurosurgical procedures has shown substantial promise in enhancing surgical precision and educational outcomes, building upon previous applications primarily focused on simple visualization tasks. This study extends the current scope by using smartphone-based AR navigation systems tailored to assist neurosurgeons performing brain tumor surgery. Unlike prior efforts, our research developed and validated an AR system that superimposes precise virtual representations of critical anatomical structures, such as the skull and brain, onto a 3D-printed patient model. These models are rendered in transparent materials that improve the visibility and understanding of intricate internal structures, facilitating more intuitive surgical planning and training. Our findings demonstrate that while AR significantly enhances the visualization of anatomical details and the alignment of virtual to physical models, its effectiveness depends on the viewing angle and facial feature distinctiveness, achieving the highest accuracy in frontal views. These results underscore improvements over traditional AR systems, which often struggle with accurate depth perception and spatial orientation, and highlight the challenges of broad integration. ©2024 IEEE.

关键词： Smartphones

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Digital Twin System for Smart Buildings Integrated with Blockchain and Mixed Reality Technology 1

Digital Twin System for Smart Buildings Integrated with Bloc...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Hidayat, Wahyu Nur Hakim, Oktafian Sultan Sukaridhoto, Sritrusta Zainuddin, Muhammad Agus Prayudi, Agus Arissabarno, Cahyo Achmad, Zacky Maulana Budiarti, Rizqi Putri Nourma Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (纸本)9798350365191

The integration of 3D innovation, Mixed Reality (MR), and blockchain in smart buildings has revolutionized the IoT sector. Visualization using 3D technology and immersive interfaces allows users to control and interact with devices within smart building environments. Users can use hand gestures or controllers in a 3D environment to adjust lighting, monitor energy consumption, or manage other smart devices. When combined with MR, the capabilities of 3D technology are enhanced with virtual elements overlaying onto the real-world environment. Within smart buildings, MR can visualize real-time sensor data, display device statuses, and provide intuitive visual feedback. The integration of blockchain in this ecosystem adds layers of security, transparency, and trust by securely recording and verifying device data, control commands, and user interactions. Blockchain technology enables secure peer-to-peer communication and smart contracts for automated device interactions and control. User testing results for controlling smart buildings with Mixed Reality (MR) technology showed high user satisfaction with an average score of 4.48. Users appreciated the performance and functionality of the system, as well as the new experience provided by the natural and immersive MR interface for interacting with smart building controls. © 2024 IEEE.

关键词： Smart homes

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Arinin, O.V. Bakhmach, D.M. Katsnelson, A.I. Kurbeteva, A.S. Yelizarov, A.A. Moscow Technical University of Communications and Informatics Moscow Russia Hse University Electronic Engineering Department Moscow Russia Hse University Computer Engineering Department Moscow Russia

ISBN: (纸本)9798331532635

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described. © 2025 IEEE.

关键词： Software packages

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Learner Assessment System in e-Learning with OBE Approach: Activity Performance, Ability Level and Recommendation

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 572-602页

作者： Yuniarti, Wenty D. Hartati, Sri Priyanta, Sigit Surjono, Herman D. Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Information Technology Universitas Islam Negeri Walisongo Semarang50185 Indonesia Department of Informatics Engineering Education Faculty of Engineering Universitas Islam Negeri Walisongo Yogyakarta55281 Indonesia

E-learning can lead learners to achieve learning outcomes if it is designed based on several principles. One is applying assessments that motivate and inform ability levels. In Outcome-based Education (OBE), assessment is integral to the system. However, e-learning has limitations in providing assessment instruments according to needs, such as assessing complex and detailed aspects and accommodating a variety of numerical and linguistic assessment data. Moreover, the presence and involvement of learners affect their performance and learning outcomes. This study proposes a learner assessment system in e-learning with the OBE approach, including learning design, activity performance analysis, ability level determination, and recommendations. This system adds the e-rubric to e-learning to overcome instrument limitations and accommodate comprehensive assessments. Various numerical and linguistic assessment data are unified using 2-tuple fuzzy linguistics, producing ability levels as two tuples. Performance analysis was based on event log data using descriptive statistical technique and alignment-based conformance checking, from frequency, time, and sequence of activity objects, resulting in five activity performance variables. The performance value of each variable is converted into High, Medium, or Low levels. The ability and performance levels are processed using rule-based methods to produce recommendations for learning stages and activity performance directions. The results of this research can be used as input for academic stakeholders and online learning providers and potentially be applied to the advancement of e-learning in higher education. © Authors retain all copyrights.

关键词： Contrastive Learning

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An intrusion detection system using ranked feature bagging

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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