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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Safari, Rahebeh Mojtahedi Rahmani, Amir Masoud Alizaeh, Sasan H. Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Tehran Iran

ISBN: (纸本)9798350348873

At present, the prediction of user retweeting behavior plays a significant role in perceiving the underlying mechanism of information diffusion in the microblogging network. The problem of retweeting behavior prediction is usually considered as a classification task hence, the effort for the classification of the time-series data in the microblogging network has gained spacious attention. It is challenging to improve the classification accuracy of retweeting behavior prediction models, because the applied classifications still lack a tailored method. To address this, we present a retweeting behavior prediction model based on Dynamic Naïve Bayes classifier that is an appropriate classifier dealing with time-series data classification. In fact, we apply a time discretization approach and incorporate user individual, social and temporal features into our proposed prediction model to estimate accurately user retweeting behavior in the microblogging network over time. The real Twitter(X) microblogging dataset is used to evaluate the performance of the proposed model in the user retweeting prediction. The experimental results show that our proposed classifier due to its simple structure can avoid overfitting problem, and outperforms in compared to other baseline methods. ©2024 IEEE.

关键词： Classification (of information)

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A K-Band CMOS Power Amplifier using an Analog Predistortion Linearizer with 22.1 dBm Psat and 0.9° AM-PM Distortion

A K-Band CMOS Power Amplifier using an Analog Predistortion ...

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IEEE Symposium on Radio Frequency Integrated Circuits (RFIC)

作者： Junhan Lim Wonseob Lee Seong-Mo Moon Euijin Oh Seunghun Wang Dongpil Chang Jinseok Park Satellite Payload Research Section ETRI Korea Department of Intelligent Electronics and Computer Engineering Chonnam National University Korea

ISBN: (数字)9798350359473

ISBN: (纸本)9798350359480

This paper presents a K-band CMOS power amplifier (PA) using the 65-nm CMOS process. To improve linearity, an analog predistortion linearizer using a cold-FET and a variable inductor is proposed. Unlike conventional cold-FET linearizers, which face limitations in improving amplitude-to-phase (AM-PM) distortion at high output power, the proposed linearizer compensates both AM-PM distortion and amplitude-to-amplitude (AM-AM) distortion of the PA. Especially, the linearizer has a phase-lag characteristic at medium output power and phase-lead characteristic at high output power, which compensates for AM-PM distortion of the PA up to high output power. The implemented PA achieved a peak power-added efficiency (PAE) of 34.4%, saturation output power (P sat ) of 22.1 dBm, and P 1dB of 19.44 dBm at 27 GHz. Also, AM-PM distortion of only 0.9° was achieved through the proposed linearization technique. Linear output power satisfying error vector magnitudes (EVMs) of -25 dB and -30 dB were measured at 16.5 dBm and 14.9 dBm, respectively.

关键词： Power measurement K-band Measurement uncertainty Linearization techniques Predistortion Radiofrequency integrated circuits Vectors

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Energy and cost benefits of advanced lighting technology applications in traffic light systems

Energy and cost benefits of advanced lighting technology app...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

作者： Alexis Polycarpou Marios S. Georgiou Department of Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus Electromechanical Section Nicosia Municipality Nicosia Cyprus

ISBN: (数字)9781837242689

Following a pilot project, which concluded in 2016, the replacement of old energy consuming Traffic Light Systems (TLS) of Nicosia Municipality were the subject of an investigation with regards to the energy saving potential and most importantly the payback period of the investment, through presumptions. This paper presents the results obtained of the new efficient technology implementation, with regards to the energy saving that materialised in the Nicosia Municipality. Investigation is presented in an attempt to determine whether the proposed solution and calculated capital employed had the expected energy usage reduction, return on investment, and payback period. The estimated cost benefit of the installed systems compared to the actual benefits varies significantly, after factors such as maintenance and electricity costs are considered.

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Refining Pseudo Labels With a Teacher-Student Paradigm for Accurate Brain Lesion Segmentation

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IEEE Access 2025年 13卷 84474-84487页

作者： Chatterjee, Pubali Das Sharma, Kaushik Chakrabarti, Amlan Department of Computer Science and Engineering Bhubaneswar751030 India University of Calcutta Electrical Engineering Section Department of Applied Physics Kolkata700073 India University of Calcutta A. K. Choudhury School of Information Technology Kolkata700073 India

In medical image segmentation, obtaining large volume of high quality labeled data is a persistent challenge, especially for intricate tasks like brain lesion segmentation, where annotations are time-consuming, costly, and require expert knowledge. This work introduces a novel learning framework where the abundance of labeled data is scarce, and the model learns from both limited number of annotated data as well as from unlabeled data. The framework initially leverages superpixel segmentation to group pixels with similar properties into compact, perceptually coherent regions while preserving critical structural details, and produce coarse segmentation mask on unlabeled data. In the next phase, a teacher-student model is employed, where the teacher module generates pseudo-labels for unlabeled data, and the student model learns from both of these pseudo-labels and minimal amount of annotated data. In addition, a feature refinement module is introduced to further strengthen the teacher-student framework, where the student module also learns from the mutual agreement between superpixel prediction and the pseudo label generated by the teacher module. The overall framework helps in efficiently using the unlabeled data with the help of handful amount of labeled data. Experimental results across multiple datasets demonstrate the effectiveness of the proposed framework in achieving precise, computationally efficient brain lesion segmentation. The integration of superpixel-based grouping and the student-teacher architecture not only improves segmentation accuracy but also offers a scalable solution for scenarios with limited annotated data. This method provides a promising pathway for advancing medical imaging applications, enabling accurate and efficient segmentation while addressing the challenges of data scarcity and annotation constraints. © 2013 IEEE.

关键词： Image segmentation

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A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

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computer Systems Science & engineering 2023年第8期46卷 1789-1809页

作者： Hicham Moujahid Bouchaib Cherradi Oussama El Gannour Wamda Nagmeldin Abdelzahir Abdelmaboud Mohammed Al-Sarem Lhoussain Bahatti Faisal Saeed Mohammed Hadwan EEIS Laboratory ENSET of MohammediaHassan II University of CasablancaMohammedia28820Morocco STIE Team CRMEF Casablanca-SettatThe Provincial Section of El JadidaEl Jadida24000Morocco Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems King Khalid UniversityMuhayel Aseer61913Saudi Arabia College of Computer Science and Engineering Taibah UniversityMedina42353Saudi Arabia Department of Computer Science University of Sheba RegionMarib14400Yemen DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Information Technology College of ComputerQassim UniversityBuraydah 51452Saudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaiz6803Yemen

Due to the rapid propagation characteristic of the Coronavirus(COVID-19)disease,manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of ***,new automated diagnostic methods have been brought on board,particularly methods based on artificial intelligence using different medical data such as X-ray *** imaging,for example,produces several image types that can be processed and analyzed by machine and deep learning methods.X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging *** this paper,we propose a novel Convolutional Neural Network(CNN)model(COV2Net)that can detect COVID-19 virus by analyzing the X-ray images of suspected *** model is trained on a dataset containing thousands of X-ray images collected from different *** model was tested and evaluated on an independent *** order to approve the performance of the proposed model,three CNN models namely Mobile-Net,Residential Energy Services Network(Res-Net),and Visual Geometry Group 16(VGG-16)have been implemented using transfer learning *** experiment consists of a multi-label classification task based on X-ray images for normal patients,patients infected by COVID-19 virus and other patients infected with *** proposed model is empowered with Gradient-weighted Class Activation Mapping(Grad-CAM)and Grad-Cam++techniques for a visual explanation and methodology debugging *** finding results show that the proposed model COV2Net outperforms the state-of-the-art methods.

关键词： Artificial intelligence intelligent diagnostic systems decisionmaking COVID-19 convolutional neural network

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Realizing Global Citizen Solutions for Sustainable Development Goals: A Collaborative engineering Course Between Thai and Japanese Students 23

Realizing Global Citizen Solutions for Sustainable Developme...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Sae-Jia, Thanachot Leelawat, Natt Tang, Jing Murakami, Rie Taoka, Yuki Ota, Eri Punyabukkana, Proadpran Chulalonkorn University Faculty of Engineering Department of Environmental Engineering Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan International School of Engineering Chulakongkorn University Faculty of Engineering Bangkok Thailand Global Education Section Center for Entrepreneurship Education Tokyo Institute of Technology Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350353952

One of the problems faced by engineering and science students is focusing solely on technical knowledge while needing more cultural context. In today's interconnected world, addressing global challenges necessitates collaborative efforts from individuals across nations. Accordingly, a joint course between Chulalongkorn University in Thailand and the Tokyo Institute of Technology in Japan has been established. The recent theme of the course, 'Gamification towards Sustainable Development Goals (SDGs),' aimed to foster global citizenship and address SDGs through multicultural collaboration. This study examines the similarities and differences in problem identification between Thai and Japanese student groups participating in the course. Data was collected from discussions, presentations, and group assignments throughout the course. The findings indicate that while both groups share common concerns, such as waste management, language barriers, and transportation issues, their perspectives and proposed solutions are influenced by cultural characteristics. Thai students emphasized infrastructural inadequacies and local traditions, whereas Japanese students focused on efficiency and the impacts of tourism. The study highlights the importance of understanding cultural nuances in collaborative international projects to achieve effective and innovative solutions to global challenges. © 2024 IEEE.

关键词： Sustainable development goals

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Integration of FDOSM and FWZIC under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

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International Journal of Information Technology and Decision Making 2024年第4期23卷 1559-1599页

作者： Alsattar, H.A. Qahtan, Sara Mohammed, R.T. Zaidan, A.A. Albahri, O.S. Kou, Gang Alamoodi, A.H. Albahri, A.S. Zaidan, B.B. Al-Samarraay, Mohammed S. Malik, R.Q. Jasim, Ali Najm Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq Department of Computer Center Middle Technical University's Baghdad Iraq Sulaymaniyah Iraq Faculty of Engineering and IT The British University in Dubia United Arab Emirates Computer Techniques Engineering Department Mazaya University College Nassiriya Thi-Qar Iraq School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Foundation of Alshuhda Baghdad Iraq

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-Attribute decision-Analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. © 2024 World Scientific Publishing Company.

关键词： COVID-19

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Measuring the social influence for retweet behavior prediction in microblogging network 11

Measuring the social influence for retweet behavior predicti...

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11th International Symposium on Telecommunication, IST 2024

作者： Safari, Rahebeh Mojtahedi Rahmani, Amir Masoud Alizaeh, Sasan H. Islamic Azad University Lahijan Branch Department of Computer Engineering Lahijan Iran National Yunlin University of Science and Technology Future Technology Research Center 123 University Road Section3 Douliou Yunlin64002 Taiwan Faculty of Information Technology Tehran Iran

ISBN: (纸本)9798350356250

Nowadays, retweeting behavior is considered a substantial behavior to analyze information diffusion in the microblogging network. To understand the underlying mechanism of information diffusion and analyze the evolution of the diffusion corresponding to a given original microblog, user retweeting behavior prediction has recently gained considerable attention. In this paper firstly, we introduce the most influential factors which affect user retweeting behavior and propose an effective measurement based on the random walk model to measure the social influence. Secondly, by incorporating our defined influential factors into a logistic regression classifier model, we predict the future activated user set concerning the diffusion of a given original microblog at the next time point. Unlike other comparative models, our prediction model can illustrate the evolution of diffusion and predict the retweeting behavior of all potential users through the diffusion of any original microblog in the microblogging network. Finally, based on the real Twitter microblogging dataset, the experimental results show by the contribution of our defined influential factors and presenting an effective measure of the social influence factor, we can obtain more accurate predictions in comparison to other baseline methods. © 2024 IEEE.

关键词： Prediction models

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Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

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Evolving Systems 2024年第2期15卷 669-686页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Garfan, Salem Albahri, A.S. Zaidan, B.B. Chen, Juliana Perak Malaysia Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunrlin Douliou64002 Taiwan Discipline of Nutrition and Dietetics Susan Wakil School of Nursing and Midwifery Faculty of Medicine and Health The University of Sydney CamperdownNSW2006 Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Charles Perkins Centre The University of Sydney CamperdownNSW2006 Australia

Mobile apps for sign language are a fascinating area of research that merits a lot of attention. These apps are widely used due to their affordability and usability. Nevertheless, the quality of these apps needs to be evaluated if they are to have a significant influence. The outcomes of app quality assessment can also stimulate app development efforts and improvements to their functionality and accuracy. Therefore, to gain more in depth understanding, four academic databases (Science Direct, IEEE, Association for Computing Machinery, and Web of Science) were searched as part of a systematic literature review. The review highlighted that sign language apps require further development to support users in achieving the greatest possible usage and learning experience. We present a solution framework based on a quality assessment criterion for sign language mobile apps that could be adopted by academics and developers. Alternative identification, criteria, and development are the three primary aspects of the framework. The results demonstrate that the new assessment approach can establish a detailed set of criteria for sign language mobile app development. Future academics could gain a thorough understanding of mobile app development criteria from this study. The results are advantageous and open up a new field where researchers and developers could collaborate on sign language mobile app development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Audition

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Optimizing Cloud Data Centers for Environment Sustainability through Performance-Aware Virtual Machine Allocation

Optimizing Cloud Data Centers for Environment Sustainability...

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International Conference on Electronics, Materials engineering and Nano-Technology (IEMENTech)

作者： Vaneet Kumar Balkrishan Jindal Department of Computer Science and Engineering Chandigarh University Gharuan India Computer Engineering Section YCoE Punjabi University Guru Kashi Campus Talwandi Sabo India

Cloud computing faces challenging issues concerning high-energy consumption and service-level agreement (SLA) violations, particularly as the demand for computational power continues to grow, necessitating large-scale cloud data centers. Existing energy-aware approaches tend to focus on performance optimization during VM migration phases but it can be effectively maintained at VM allocation level as preprocessing. Thereby, this paper proposed Energy Efficient SLA-aware virtual machine Allocation approach using a metaheuristic Firefly algorithm (FFO-EESA). The experimental findings showcase promising outcomes in the context of dynamic workloads and varying numbers of VMs (ranging from 1 to 290) allocated on individual hosts. The proposed method’s experimental results were obtained through computer-based simulations conducted in MATLAB version 2017(a). The outcomes showed that, there was a reduction of 12.34% in migrations and a saving of 4.36% in idle hosts with the FFO-EESA approach. proposed algorithm resulted in an average energy savings of 4.39% compared to the FF and MBFD techniques.

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