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5th International IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2023

作者： Eltayab, Engy N Al-Atabany, Walid Salem, Nancy M. Mahmoud, Lamees N. Helwan University Biomedical Engineering Department Egypt Nile University Information Technology and Computer Science School Egypt

ISBN: (纸本)9798350381030

Nuclei segmentation is a critical task in biological image analysis, with numerous applications in cancer diagnosis, grading, staging, and treatment planning. However, this task is challenging, particularly when dealing with low-resolution and low signal-to-noise ratio microscopy images. Segmentation problems arise, such as touching and missing cells, which make the process even more challenging. Deep learning models, including Attention U-Net and TransUNet, have demonstrated exceptional performance in medical image segmentation. Nonetheless, the choice of optimizer can significantly impact the model's performance, especially in nuclei segmentation. In this work, we assessed the efficiency of the latest deep learning optimizers, such as Ranger and GC, on Attention U-Net and TransUNet models and compared their performance with traditional optimizers, i.e., Adam. We conducted experiments on the publicly available Fluo-N2DH-HeLa dataset that was adapted from the cell tracking challenge. The Ranger optimizer, along with the TransUNet model, achieved the highest mean IOU of 95.05% and reasonable recall and precision scores of 94.4% and 96.8%, respectively. The experimental results proved that integrating the TransUNet model with the Ranger optimizer significantly enhanced touching nuclei segmentation performance in fluorescence microscopy images. © 2023 IEEE.

关键词： Grading

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PREDICTION OF OVARIAN SYNDROME USING DEEP FEATURE REPRESENTATION AND LEARNING APPROACHES

PREDICTION OF OVARIAN SYNDROME USING DEEP FEATURE REPRESENTA...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sherubha, P. Madhumitha, R. Sasi Rekha, S.P. Arivazhagi Department of Information Technology Karpagam College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer Science and Engineering University College of Engineering Ariyalur India

ISBN: (纸本)9798350305708

The high incidence of the polycystic ovarian syndrome (PCOS) among fertile women receives more and more attention. Women's physical and mental health may be at risk due to the common disorder PCOS, whose identification is an expanding issue in worldwide public health. In this work, we looked into the potential significance of abnormalities in the identification of PCOS and provided information-based feature representation and learning techniques (IFRL) for PCOS auxiliary identification. Here, a real-time dataset is acquired from the Coimbatore region by examining 74 individuals, and these samples are used in the algorithm. The suggested method performs pre-processing, feature learning and classification using the proposed IFRL. To achieve categorization, a multi-instance model was finally built. Several performance indices were used to evaluate our method's effectiveness, including AUC, precision, recall, accuracy, and F1-score for classification. © 2023 IEEE.

关键词： Classification (of information)

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Remote Heart Rate Estimation Based on ROI Extraction 16

Remote Heart Rate Estimation Based on ROI Extraction

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Jian, Wei Zhang, Yuqi Hu, Ke Fudan University Center for Biomedical Engineering School of Information Science and Technology Shanghai China Fudan University School of Computer Science Shanghai China Fudan University School of Information Science and Technology Shanghai China

ISBN: (纸本)9798350330755

Remote photoplethysmography (rPPG) has acquired much attention in health monitoring and emotion detection in recent years due to its convenience by non-contact approaches. Most studies have primarily concentrated on the estimation of pulse signals methods from the region of interest (ROI). Due to the unequal distribution of rPPG signals on the facial parts, accurate extraction of the ROI is a critical process for acquiring dependable pulse signals. However, many of those methods for ROI extraction encounter challenges, such as background pixels and non-critical skin pixels existing in the ROI. This paper proposes an effective framework integrating face detection and segmentation to address the remote heart rate estimation in presence of the ROI extraction issues. The experimental results demonstrate the proposed framework can efficiently extract skin pixels carrying high-quality heart rate information. As a result, it achieves a significant improvement in the performance of heart rate estimation. © 2023 IEEE.

关键词： Face recognition

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FlashHack: Reflections on the Usage of a Micro Hackathon as an Assessment Tool in a Machine Learning Course 2024

FlashHack: Reflections on the Usage of a Micro Hackathon as ...

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1st ACM Virtual Global Computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Indra, R. Parthasarathy, P.D. Ambasana, Jatin Satavlekar, Spruha Department of Information Science and Engineering B.M.S. College of Engineering Bengaluru India Department of Computer Science and Information Systems Bits Pilani KK Birla Goa Campus Goa India Centre for Educational Technology Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798400705984

Machine learning (ML) course for undergraduates face challenges in assessing student learning and providing practical exposure. Group project-based learning, an increasingly popular form of experiential learning in CS education, encounters certain limitation in participation and non-participation from a few students. Studies also suggest that students find longer programming assignments and project-based assessments distracting and struggle to maintain focus when they coincide with other courses. To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon that combines project-based learning with Hackathon elements. Engaging 229 third year CS undergraduate students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe. Assessment criteria emphasized machine learning application, problem-solving, collaboration, and creativity. Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors. This experience report outlines the Hackathon design and implementation, highlights successes and areas for improvement making it feasible for replication by interested computing educators. © 2024 ACM.

关键词： Adversarial machine learning

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Success Factors of Enterprise Resource Planning: A State-of-the-Art Mini Review

Success Factors of Enterprise Resource Planning: A State-of-...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： BaniMustafa, Ahmed Bulkrock, Olla Faculty of Computer Information Science Higher Colleges of Technology Abu Dhabi United Arab Emirates Software Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798350353488

Enterprise Resource Planning Systems (ERP) are vital for today’s businesses. However, the successful implementation of ERP systems faces several challenges, which can determine its success or failure. This paper provides a brief state-of-the-art review of the success factors of ERP implementation using various applications that span different contexts. The paper discusses and compares the aims defined and the techniques applied in these studies, as well as the data used and the results obtained from each of these studies. This review paper reviews published work to tackle these challenges, covering the literature published in 2020-2023. A special effort was also made to focus on high-quality papers published in top journals with high-impact factors and indexed in Scopus and Web of science. The review process is repeatable, as special keywords were used to search the literature using publishers’ databases and three specialized tools. The paper concludes by discussing the future of the successful implementation of ERP systems. The paper argues that ERP systems will continue to have a major impact on the success of today’s businesses. Therefore, they must carefully consider various factors that influence their successful implementation. © 2024 IEEE.

关键词： information use

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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A Deep Learning Method for Statistical Downscaling of CLDAS Relative Humidity with Different Sources of Data:Sensitivity Analysis

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Journal of Meteorological Research 2023年第6期37卷 878-895页

作者： Bin BAI Chunxiang SHI Ling YANG Lingling GE Luhui YUE Guangyu LIU College of Electronic Engineering Chengdu University of Information TechnologyChengdu610225China Key Laboratory of Atmospheric Sounding of China Meteorological Administration Chengdu610225China National Meteorological Information Centre China Meteorological AdministrationBeijing100081China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

High-resolution relative humidity(RH)data are essential in studies of climate change and in numerical meteorological ***,because high-resolution meteorological grid data require a large number of stations,the sparse distribution of ground meteorological stations in China before 2008 has limited the development of long-term and high-resolution RH products in the China Meteorological Administration’s Land Assimilation System(CLDAS)*** retrieve high-quality and high-resolution RH data before 2008,we propose a statistical downscaling model(SDM)based on a generative adversarial network(GAN)to transform the original RH data from a resolution of0.05°to 0.01°.The GAN-based SDM(GSDM)is trained with the RH of the CLDAS(0.05°)dataset after 2008 as its input,and the RH of the high-resolution CLDAS(HRCLDAS,0.01°)dataset after 2008 as its target for ***2-m air temperature data from the HRCLDAS dataset are also included in the input,and the station observations of RH are incorporated in the target for *** select the optimum data combination for the model,we compared three methods:(1)incorporating without auxiliary data(GSDM),(2)incorporating air temperature as an additional input(GSDM_T),and(3)incorporating air temperature as an additional input and the RH data at stations as an additional target for training(GSDM_TO).Taking the Beijing–Tianjin–Hebei region as an example,we trained the GSDM by using data from 2018 and tested the model performance in *** experimental results showed that the GSDM_TO algorithm achieved the lowest root-mean-square error(3.85%),followed by the GSDM_T(4.01%)and GSDM(4.95%)*** proposed models showed a competitive performance and captured more local details of the RH fields than other deep learning models and traditional bilinear *** general,the GSDM_TO algorithm using a combination of different sources of data(air temperature and observed RH)achieved the best results among the various d

关键词： relative humidity statistical downscaling generative adversarial network(GAN) temperature

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Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： Deep Learning Algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation Synthetic Aperture Radar (SAR) Imagery

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The Assessment of the Design Risk for ORAN

The Assessment of the Design Risk for ORAN

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Shiang-Jiun Chang, YaHan Raksasook, Nattapol Wu, Wei-Liang National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology International Electrical Engineering and Computer Science Taipei Taiwan University of Taipei Department of Mathematics Taiwan

ISBN: (纸本)9798350324174

The Open Radio Access Network (O-RAN) uses a virtualization architecture to disaggregate components and connect them through open interfaces, and also uses software defined networking (SDN) technology for decoupling. The O-RAN architecture is an innovative telecommunication architecture, which also brings some potential problems for security, including but not limited to the followings: new architecture (disaggregation, Real Time RAN intelligent controllers (Near-RT RIC), virtualization), the open interfaces (O1, A1, and E2, etc.), artificial intelligent and machine learning (AIML) workflow, and standardization issues. Facing the new structure, in addition to the original risks, new risks also need to be considered. "Secure in design"is imperative to mitigate the information security risks. This study will look at the risk assessment of O-RAN from the perspective of testing and do security analysis. © 2023 IEEE.

关键词： Machine learning

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Importance of Blackbody in Everyday Infrared Thermography 32nd

Importance of Blackbody in Everyday Infrared Thermography

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Glavaš, Hrvoje Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9783031514937

Infrared thermography is a method of detecting thermal radiation, which is then associated with corresponding temperature values. Infrared thermal infrared cameras, like other measuring devices, must undergo a factory and periodic calibration process. The calibration is performed using blackbodies. This paper analyzes the practical application of the blackbody in the calibration of infrared thermal camera. Two infrared thermal cameras Flir E6 and Hikmicro M30 were calibrated with a Voltcraft IRS -350 blackbody. The calibration results are presented in tabular form, while the effect of the blackbody uncertainty on the overall measurement uncertainty is presented in graphical form. Since blackbodies are prohibitively expensive for most thermographers due to their price, calibration guidelines are provided to users when a blackbody is not available and the accuracy of the camera needs to be verified. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Calibration

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