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4th International Conference on Electrical engineering and Computer science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

The use of technology and information devices contributes to global warming. This issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. This research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. This research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. The approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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Diversity-Optimized Group Extraction in Social Networks

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IEEE Transactions on Computational Social Systems 2024年第1期11卷 756-769页

作者： Hsu, Bay-Yuan Chen, Yi-Ling Ho, Ya-Chi Chang, Po-Yuan Chang, Chih-Chieh Shia, Ben-Chang Shen, Chih-Ya National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu30013 Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Hsinchu31040 Taiwan Fu Jen Catholic University Department of Business Administration New Taipei City24205 Taiwan National Taiwan University of Science and Technology Management Undergraduate Program Taipei10607 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

In this article, we propose to study a novel research problem to boost group performance, that is, social-aware diversity-optimized group extraction (SDGE), which takes into consideration the two important factors: 1) group diversity and 2) social tightness. We prove the NP-hardness of SDGE and propose an effective algorithm, named group shrinking for diversity maximization (GSDM) with a performance guarantee, that is, GSDM is a three-approximation algorithm to the SDGE problem studied in this article. We further propose three effective pruning strategies that are able to boost the efficiency of GSDM but do not deteriorate its performance. We conduct extensive experiments on multiple large-scale real datasets to evaluate the performance of GSDM. The experimental results show that our proposed GSDM outperforms the other baseline approaches significantly, in terms of solution quality and efficiency. Moreover, the experimental results also confirm that our proposed pruning strategies indeed boost the efficiency of the algorithm. © 2014 IEEE.

关键词： Costs

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Advances in 3D fusion of multimodal medical images: 3D reconstruction of bone, muscle, and ligament structures under load from radiographs and magnetic resonance imaging

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Research on Biomedical engineering 2025年第1期41卷 1-35页

作者： da Silva, Daniel S. Astolfi, Rodrigo S. Jagatheesaperumal, Senthil K. Selvaraj, Priyadharsini Russel, Newlin Shebiah de Albuquerque, Victor Hugo C. Post-Graduation Program of Medical Surgical Sciences Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Teleinformatics Engineering Federal University of Ceara CE Fortaleza60455-970 Brazil Department of Electronics and Communication Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India Department of Computer Science and Engineering Mepco Schlenk Engineering College TN Sivakasi626005 India

Purpose: Foot and ankle pathologies are among the most prevalent conditions within the human locomotor system. Imaging examinations are crucial for diagnosing, treating, and achieving satisfactory functional outcomes for these conditions. However, conventional imaging exams often fail to capture all the necessary details for precise analysis, necessitating the use of multiple imaging modalities to evaluate bones, ligaments, cartilage, and other structures. Objective: This research aims to explore computational technologies and algorithms for 3D reconstruction and fusion of foot and ankle images, alongside the analysis, segmentation, classification, and evaluation techniques applied to these images. Methods: The study investigates different computational approaches for 3D reconstruction and multimodal image fusion, integrating X-ray and MRI data. Advanced segmentation and classification techniques are applied to enhance the precision of structural assessment. Additionally, we assess the availability and quality of databases for training and validating these models. Results: Our findings highlight a significant gap in research concerning these specific applications, as well as a shortage of suitable databases for 3D reconstruction and fusion of images from X-rays and MRI. Conclusion: The study emphasizes the need for dedicated datasets and further research to develop effective computational solutions for multimodal foot and ankle imaging. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Magnetic resonance imaging

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Two-dimensional RGO-bridge S-scheme phosphorus-doped g-C_(3)N_(4)/Bi_(5)O_(7)Ivan der Waals heterojunctions for efficient visible-light photocatalytic treatment of real pharmaceutical wastewater

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science China(Technological sciences) 2023年第10期66卷 3011-3024页

作者： WANG ZhenZhou XU XiWei DENG Fang DIONYSIOU Dionysios D LI XiBao PENG JunLong LUO XuBiao ZOU JianPing LUO ShengLian National-Local Joint Engineering Research Center of Heavy Metal Pollutants Control and Resource Utilization Nanchang Hangkong UniversityNanchang 330063China Environmental Engineering and Science Program Department of Chemical and Environmental Engineering(DChEE)University of CincinnatiCincinnati 45221-0012USA

The design of van der Waals heterojunctions with S-scheme charge transfer pathway is expected to be an effective strategy for improvement of photocatalytic ***,two-dimensional(2D)phosphorus-doped g-C_(3)N_(4)/Bi_(5)O_(7)Ivan der Waals heterojunctions with reduced graphene oxide as electron bridge(PCN/RGO/Bi_(5O)_(7)I)were successfully synthesized via hydrothermal *** van der Waals interaction endowed 2D PCN/RGO/Bi_(5O)_(7)I with intimate contact interface,lattice match,tunable band structure,and internal electric field,which efficiently promoted interfacial charge separation and enhanced redox ability of photogenerated charge *** a result,the S-scheme PCN/RGO/Bi_5O_7I van der Waals heterojunctions exhibited superior photocatalytic performance in ciprofloxacin degradation and real pharmaceutical wastewater *** optimized 12%PCN/RGO/Bi_5O_7I displayed the highest photocatalytic activity with 92%removal of ***,the COD removal efficiency and extent of mineralization of real pharmaceutical wastewater reached 66.9%and 59.8%,respectively,and the biodegradability of pharmaceutical wastewater was significantly *** photocatalytic mechanism of the S-scheme PCN/RGO/Bi_(5)O_(7)I van der Waals heterojunctions based on the analysis of reactive species,work function,and internal electric field was *** study provides fresh insights into plausible design of S-scheme van der Waals heterojunction to enhance photocatalytic redox ability.

关键词： S-scheme heterojunction van der Waals heterostructure photocatalytic degradation ciprofloxacin energy consumption

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Enhancing Healthcare Monitoring Systems: A Case Analysis in Optimization 18

Enhancing Healthcare Monitoring Systems: A Case Analysis in ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Lalit, Mohit Bathla, Gaurav Singh, Surender Chandigarh University Computer Science and Engineering Department Mohali India Career Program Code Quotient Mohali India

ISBN: (纸本)9789380544519

In the past few years, fog computing (FC) has emerged as a promising complement to cloud computing. It offers reduced latency, minimal bandwidth consumption, and real-time data transfer. In healthcare, particularly in IoT-enabled systems, FC integration has become pivotal for real-time monitoring and immediate data transfer to health experts. The researchers all around the world proposed and developed various communication frameworks and come up with certain outputs in terms of quality-of-service parameters (QoS). However, these QoS parameters are not always appropriate for effective execution of the application. Following that, this study explores the integration of FC with multi-objective Optimization Algorithms (such as FFLY and GWO) to optimize crucial quality of service (QoS) metrics using a proposed linear function to optimize multi objectives, essential for sustained communication. The research focuses on optimizing multi objectives in healthcare monitoring systems. Experimental results reveal the superiority of the Grey Wolf Optimizer (GWO) over the Firefly Algorithm (FFLY). This study emphasizes the crucial significance of multi-objective optimisation algorithms in optimizing critical parameters for successful healthcare communication frameworks, which can lead to improvements in healthcare monitoring efficiency. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Fog Computing Healthcare 4.0 Industry 4.0 IoT Optimization

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Introduction to this special section: Geophysical methods in archaeology

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Leading Edge 2024年第10期43卷 655-656页

作者： Krahenbuhl, Richard A. Wilt, Michael Colorado School of Mines Geophysics Department Humanitarian Engineering and Science Interdisciplinary Graduate Program GoldenCO United States Lawrence Berkeley National Laboratory Earth Science Division BerkeleyCA United States

Archaeogeophysics, i.e., the application and integration of geophysics into archaeological investigations, is an exciting and growing field of study and an international collaboration at the intersection of the physical and social sciences. Through the incorporation of ground-and drone-based geophysical and remote sensing tools, these collaborative teams continue to advance the field of archaeology by rapidly and accurately revealing hidden features of the past and helping to focus active archaeological digs. Equally relevant, yet often unnoticed, the inclusion of noninvasive geophysical tools likewise provides a means to understand and preserve historically and culturally sensitive sites without the need for extensive and potentially destructive excavations. © 2024 by The Society of Exploration Geophysicists.

关键词： Geophysics

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Dense carbon nanofiber self-supporting electrode fabricated by orientation/compaction strategy for high volumetric lithium storage capacity

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Progress in Natural science:Materials International 2025年第1期35卷 229-237页

作者： Haihui Liu Chaoqun Chen Qiang Xu Yan Song Xiangwu Zhang Chang Ma Tianjin Municipal Key Lab of Advanced Fiber and Energy Storage Technology Tiangong University CAS Key Laboratory of Carbon Materials Institute of Coal Chemistry Chinese Academy of Sciences Fiber and Polymer Science Program Department of Textile Engineering Chemistry and Science Wilson College of Textiles North Carolina State University

Low bulk density greatly restricts the large-scale application of electrospun carbon-based fiber membrane as electrode in energy storage devices. To solve the above challenges, herein an orientation-compaction densification strategy is proposed to enhance the bulk density and volumetric capacity of PAN-based carbon nanofiber membranes as self-supporting electrode used in lithium-ion batteries（LIBs）. Specifically, highly-oriented fibers are achieved by high-speed roller collecting during electrospinning, and compaction densification is conducted by hot-pressing treatment. The effects of collecting speed and hot-pressing pressure on the morphology, conductivity,bulk density, tensile strength, and flexibility of the obtained carbon nanofiber membrane are *** to conventional fiber membranes, of which fibers are disorderly stacked, the oriented fiber membrane is much easier to achieve dense stacking by compaction. The obtained dense carbon nanofiber membrane demonstrates a bulk density of 0.566 g cm-3, and shows a significantly-enhanced volumetric capacity(318.3 mA h cm-3), high-rate performance(86.6 mA h cm-3at 5 A g-1), and satisfactory cycling stability when used as selfsupporting electrode of LIBs.

关键词： Bulk density Densification Electrospinning Orientation-compaction Volumetric capacity

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