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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Yulei Wang Qianming Zhou College of Computer Science Xi'an Polytechnic University Xi’an China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Xi’an China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In this study, to address the problems of multi-source data input, low classification accuracy, and fuzzy segmentation boundaries in the terrain classification task, NASADEM is used as the data source, and part of the Shaanxi Province of China is intercepted as the dataset, so as to propose an optimized semantic segmentation network model, GMF-HRNet, to achieve efficient and automatic classification of the basic landform types. Initially, we introduce a Gated Attention Block (GA Block) to efficiently extract terrain feature information and enhance semantic information. Subsequently, in order to take into account the complementarity and inherent dissimilarity among features of varying scales, a Multi-scale Gradual Feature Fusion module (MGFF) is devised to aggregate global and local information, thereby significantly improving classification accuracy. The experimental results show that the mean Intersection over Union (mIoU) of GMF-HRNet reaches 78.16%, which is improved by 3.73% compared with the original model, and at the same time exceeds the mainstream semantic segmentation network model, which has a wide range of application prospects in the research field of the intersection of geography and computer disciplines.

关键词： Geography Temperature distribution Accuracy Computational modeling Semantic segmentation Soft sensors Semantics Feature extraction Data models Reliability

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Application of Data science and Machine Learning in the Prediction of College Dropout: A Data-Driven Predictive Approach

Application of Data Science and Machine Learning in the Pred...

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International Conference on Software Process Improvement (CIMPS)

作者： Axel Frederick Félix Jiménez Vania Stephany Sanchéz Lee Isaul Ibarra Belmonte Ezra Federico Parra González Engineering in Computer Systems National Polytechnic Institute Zacatecas México Software Engineer Center for Research in Mathematics Zacatecas México Computer Science Department Center for Research in Mathematics Zacatecas México

ISBN: (数字)9798350358568

ISBN: (纸本)9798350358575

This paper presents a study on the prediction of student graduation or failure using two predictive models: K-Nearest Neighbors (KNN) and a forward sequential Artificial Neural Network (ANN). The models, built with a well-chosen set of independent variables, were assessed using metrics like precision and accuracy. The results obtained revealed that both the KNN model and the sequential forward ANN model achieved high efficiency in predicting student graduation or failure, achieving accuracies of 0.9133% (K=3) and 0.9312% (after 50 epochs), respectively. Providing a valuable tool to identify early on students at risk of not graduating and to take preventive measures to improve their academic performance. Comparisons with related research showed consistent outcomes, underscoring the credibility and importance of the employed predictive models.

关键词： Measurement Artificial neural networks Machine learning Predictive models Data science Software

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Graph-Powered Reinforcement Learning for Intelligent Task Offloading in Vehicular Networks

Graph-Powered Reinforcement Learning for Intelligent Task Of...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ihsan Ullah Hyun-Kyo Lim Yeong-Jun Seok Naeem Ul Islam Chang-Hun Ji Youn-Hee Han Advanced Technology Research Center KOREA TECH Cheonan South Korea Quantum Network Research Center KISTI Daejeon South Korea Computer Science and Engineering KOREA TECH Cheonan South Korea Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Future Convergence Engineering KOREA TECH Cheonan South Korea

In vehicular edge computing, task offloading optimization is crucial for balancing computational demands with minimizing delays and costs. However, the dynamic nature of the vehicular environment, including vehicle mobility, network topology, and available computing resources, poses significant challenges. This paper presents a task offloading scheme that enables vehicles to dynamically decide between local task execution and offloading to nearby vehicles, edge servers, or the cloud. Our objective is to optimize task offloading by minimizing cost and delay. We integrate graph convolutional networks with deep reinforcement learning to optimize task offloading decisions and achieve our goal. The Graph Convolutional Network is integrated with Deep Reinforcement Learning to enhance network representation and decision efficiency of agent. The optimization problem is formally formulated within the framework of a Markov Decision Process. Simulation results demonstrate the superiority of proposed scheme, which achieves cost-efficiency by maximizing resource utilization, minimizing costs, and optimizing task offloading while reducing task rejection.

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CNN segmentation of skin melanoma in pre-processed dermoscopy images 2

CNN segmentation of skin melanoma in pre-processed dermoscop...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Verdú, Elena Damasevicius, Robertas Abualigah, Laith Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu's thresholding, (iii) Kapur's thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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The Effect of Deep Learning Segmentation on Placental T2∗ Estimation 22

The Effect of Deep Learning Segmentation on Placental T2∗ E...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yuan, Zhen Schabel, Matthias C David, Anna L. Roberts, Victoria H.J. Melbourne, Andrew School of Biomedical Engineering and Image Sciences King's College London London United Kingdom Oregon Health & Science University Advanced Imaging Research Center Portland United States University of Utah Utah Center for Advanced Imaging Research Salt Lake City United States Ucl Ega Institute for Women's Health University College London London United Kingdom Oregon Health & Science University Division of Reproductive & Developmental Sciences Portland United States

ISBN: (纸本)9798331520526

The placenta plays a crucial role in ensuring adequate oxygen delivery to the fetus. Despite being one of the most important organs in pregnancy, the placenta remains poorly understood. research into placental developmental structure and function has accelerated in recent years. The use of a preclinical pregnant nonhuman primate model allows researchers to quantify blood and oxygen transfer through the placenta and potentially improve our understanding of it. Furthermore, deploying Magnetic Resonance Imaging (MRI) to better understand the explicit function and mechanism of the placental vascular supply has become commonplace. Among these MRI techniques, T2, T2∗ and diffusivity are the most commonly deployed for accessing the pseudo-quantitative physical properties of the placenta. To enable better analysis of the placenta from MR images, and reduce the labour-intensive workload of manually delineating regions of interest, here we deployed a robust deep learning model trained on T2∗ MR images acquired from pregnant rhesus macaques, with the ground truth placenta segmentation drawn by our experts. After obtaining the trained model, we experimented with three segmentation fusion strategies aimed at merging the estimated segmentations from multi-echo times into one final outcome. For each fusion method, we conducted experiments to evaluate the effect of the automated segmentation on T2∗ estimation, including varying the echo time and the number of imaging slices. The best Dice score (averaging fusion method over 6 segmentations from different TE in one scan) reached 0.869. This work highlights the robustness of our trained segmentation model on nonhuman primate datasets and its potential for application in future preclinical studies. With appropriate fine-tuning, it could also be adapted for analysing human placental datasets. © 2025 IEEE.

关键词： Deep Learning Estimation MRI Placenta Seg-mentation Nonhuman Primate T2

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Scalable Space-Efficient Antenna Array Techniques Using Convolved Electric and Magnetic Currents

Scalable Space-Efficient Antenna Array Techniques Using Conv...

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European Conference on Microwave

作者： Amirhossein Askarian Pascal Burasa Jianping Yao Zhenguo Lu Ke Wu Poly-Grames Research Center Polytechnique Montreal Canada School of Electrical Engineering and Computer Science University of Ottawa Canada Advanced Electronics and Photonics Research Center the National Research Council Canada (NRC) Canada

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

We propose and investigate two methodologies for the design and realization of scalable low-footprint multi-band antenna arrays through the interleaving of electric and magnetic currents on mutually shared radiating surfaces. In our earlier approach, we successfully engineered and fabricated a highly isolated dual-wideband prototype operating over Ku-band, respectively showcasing impedance and gain bandwidth of approximately 42%. Furthermore, it demonstrates operability within Ka-band, respectively exhibiting impedance and gain bandwidth of 29% and 16.32%. Additionally, we have developed and fabricated a space-efficient 2×4 dual-band array designed to operate over both Ku- and Ka-bands. Our measurement results suggest that the proposed antenna array technique achieves impedance and gain bandwidth of 30% and 25.4% within Ku-band and 10.65% and 8.52% within Ka-band, respectively.

关键词： Antenna measurements Microwave measurement Surface impedance Current measurement Prototypes Bandwidth Microwave theory and techniques Transceivers Microwave antenna arrays Impedance

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Self-assembled microtubular electrodes for on-chip low-voltage electrophoretic manipulation of charged particles and macromolecules

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Microsystems & Nanoengineering 2022年第1期8卷 341-352页

作者： Apratim Khandelwal Nagendra Athreya Michael Q.Tu Lukas L.Janavicius Zhendong Yang Olgica Milenkovic Jean-Pierre Leburton Charles M.Schroeder Xiuling Li Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Chemical Engineering University of Illinois Urbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIL 61801USA Department of Electrical and Computer Engineering Microelectronics Research CenterUniversity of TexasAustinTX 78758USA Coordinated Science Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA

On-chip manipulation of charged particles using electrophoresis or electroosmosis is widely used for many applications,including optofluidic sensing,bioanalysis and macromolecular data *** hereby demonstrate a technique for the capture,localization,and release of charged particles and DNA molecules in an aqueous solution using tubular structures enabled by a strain-induced self-rolled-up nanomembrane(S-RuM)***-in 3D electrodes that are embedded in cylindrical S-RuM structures and biased by a constant DC voltage are used to provide a uniform electrical field inside the microtubular *** charged-particle manipulation is achieved at a bias voltage of<2-4 V,which is~3 orders of magnitude lower than the required potential in traditional DC electrophoretic ***,Poisson-Boltzmann multiphysics simulation validates the feasibility and advantage of our microtubular charge manipulation devices over planar and other 3D variations of microfluidic *** work lays the foundation for on-chip DNA manipulation for data storage applications.

关键词： electrodes cylindrical planar

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COVID19 detection in chest x-ray using vision-transformer with different patch dimensions 2

COVID19 detection in chest x-ray using vision-transformer wi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kadry, Seifedine Abualigah, Laith González Crespo, Rubén Verdú, Elena Damasevicius, Robertas Singh, Vijendra Rajinikanth, Venkatesan Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de la Rioja Logroño26006 Spain Department of Applied Informatics Vytautas Magnus University Kaunas53361 Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India

computerized disease detection systems (CDDs) have proven effective for automatic screening in recent years. Among the standard procedures in hospitals for faster and more accurate diagnosis is medical imaging-based disease screening. We aim to develop a CDD that detects COVID-19 using chest X-rays pre-trained vision transformers (PVTs). This scheme includes the following steps: (1) collecting images and resizing them, (2) implementing PVT for feature extraction, and (3) binary classifying the results and validating the proposed schemes. To prove the merit of the developed scheme, 4800 images (2400 normal and 2400 COVID-19) are analyzed. MLP classifiers verify the PVT performance using patch sizes of 6, 12, and 24. A patch size 24 results in 97.5% accuracy for the proposed CDD system. When patch sizes are increased to 12, accuracy increases to over 98%. For this specific task, smaller patch sizes are more effective. These high-accuracy results demonstrate the effectiveness of the developed scheme for detecting COVID-19 in chest X-rays. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diagnosis

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Automatic liver-vessel examination from CT slice using Kapur's thresholding and watershed algorithm 2

Automatic liver-vessel examination from CT slice using Kapur...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur's tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme. © 2024 Elsevier B.V.. All rights reserved.

关键词： Medical imaging

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