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arXiv 2024年

作者： Shi, Yongyi Niu, Chuang Simpson, Amber L. De Man, Bruno Do, Richard Wang, Ge Biomedical Imaging Center Department of Biomedical Engineering School of Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Biomedical Computing and Informatics Queen’s University ONK7L 3N6 Canada GE HealthCare Technology & Innovation Center NiskayunaNY12309 United States Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States

CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative diffusion prior to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases. Copyright © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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Application of graph-curvature features in computer-aided diagnosis for histopathological image identification of gastric cancer

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Intelligent Medicine 2024年第3期4卷 141-152页

作者： Ruilin He Chen Li Xinyi Yang Jinzhu Yang Tao Jiang Marcin Grzegorzek Hongzan Sun Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110016China Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110016China School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China Institute for Medical Informatics University of LuebeckLuebeckGermany Department of Knowledge Engineering University of Economics in KatowiceKatowicePoland Shengjing Hospital of China Medical University ShenyangLiaoning 110136China

Background Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors;however,it has some *** study explored a computer-aided diagnostic method that can be used to identify benign and malignant gastric cancer using histopathological *** The most suitable process was selected through multiple experiments by comparing multiple meth-ods and features for ***,the U-net was applied to segment the ***,the nucleus was extracted from the segmented image,and the minimum spanning tree(MST)diagram structure that can cap-ture the topological information was *** third step was to extract the graph-curvature features of the histopathological image according to the MST ***,by inputting the graph-curvature features into the classifier,the recognition results for benign or malignant cancer can be *** During the experiment,we used various methods for *** the image segmentation stage,U-net,watershed algorithm,and Otsu threshold segmentation methods were *** found that the U-net method,combined with multiple indicators,was the most suitable for segmentation of histopathological *** the feature extraction stage,in addition to extracting graph-edge and graph-curvature features,several basic im-age features were extracted,including the red,green and blue feature,gray-level co-occurrence matrix feature,histogram of oriented gradient feature,and local binary pattern *** the classifier design stage,we exper-imented with various methods,such as support vector machine(SVM),random forest,artificial neural network,K nearest neighbors,VGG-16,and *** comparison and analysis,it was found that classifica-tion results with an accuracy of 98.57%can be obtained by inputting the graph-curvature feature into the SVM classifier.

关键词： Gastric cancer Graph-curvature feature Image identification

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An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels

arXiv

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arXiv 2024年

作者： Suzuki, Yudai Aoki, Shiori Key, Fabian Endo, Katsuhiro Matsuda, Yoshiki Tanaka, Shu Behr, Marek Muramatsu, Mayu Quantum Computing Center Keio University Yokohama Kanagawa Japan Graduate School of Science and Technology Keio University Yokohama Kanagawa Japan Institute of Lightweight Design and Structural Biomechanics TU Wien Austria Tsukuba Ibaraki Japan Fixstars Corporation Tokyo Japan Green Computing System Research Organization Waseda University Tokyo Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Kanagawa Japan Keio University Tokyo Japan Keio University Tokyo Japan Computational Analysis of Technical Systems RWTH Aachen University Germany Department of Mechanical Engineering Keio University Yokohama Kanagawa Japan

Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization. © 2024, CC BY-NC-ND.

关键词： Shape optimization

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Classification of Soil Images using Convolution Neural Networks

Classification of Soil Images using Convolution Neural Netwo...

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2021 IEEE International Conference on Communications, computing, Cybersecurity and informatics, CCCI 2021

作者： Chatterjee, Kajal Obaidat, Mohammad S. Samanta, Debabrata Sadoun, Balqies Islam, Sk Hafizul Chatterjee, Rajdeep University Department of Computer Science Karnataka Bangalore India University of Sharjah College of Computing and Informatics Sharjah27272 United Arab Emirates Al-Balqa' Applied University College of Engineering Al Salt Jordan Indian Institute of Information Technology Kalyani Department of CSE West Bengal 741235 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9781665432085

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. © 2021 IEEE.

关键词： Soils

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A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer

arXiv

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arXiv 2024年

作者： Hashim, Fatma A. Mostafa, Reham R. Khurma, Ruba Abu Qaddoura, Raneem Castillo, P.A. Faculty of Engineering Helwan University Egypt University of Sharjah Sharjah27272 United Arab Emirates Department of Information System Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan School of Computing and Informatics Al Hussein Technical University Amman Jordan Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and effic

关键词： Global optimization

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Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning and Concatenative Synthesis 14th

Gesture-Timbre Space: Multidimensional Feature Mapping Using...

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14th International Symposium on Perception, Representations, Image, Sound, Music, CMMR 2019

作者： Zbyszyński, Michael Di Donato, Balandino Visi, Federico Ghelli Tanaka, Atau Department of Computing Goldsmiths University of London LondonSE14 6NW United Kingdom Informatics Department University of Leicester University Road LeicesterLE1 7RH United Kingdom Gesture Embodiment and Machines in Music School of Music in Piteå Luleå University of Technology Snickargatan 20 Piteå941 63 Sweden

ISBN: (纸本)9783030702090

This chapter explores three systems for mapping embodied gesture, acquired with electromyography and motion sensing, to sound synthesis. A pilot study using granular synthesis is presented, followed by studies employing corpus-based concatenative synthesis, where small sound units are organized by derived timbral features. We use interactive machine learning in a mapping-by-demonstration paradigm to create regression models that map high-dimensional gestural data to timbral data without dimensionality reduction in three distinct workflows. First, by directly associating individual sound units and static poses (anchor points) in static regression. Second, in whole regression a sound tracing method leverages our intuitive associations between time-varying sound and embodied movement. Third, we extend interactive machine learning through the use of artificial agents and reinforcement learning in an assisted interactive machine learning workflow. We discuss the benefits of organizing the sound corpus using self-organizing maps to address corpus sparseness, and the potential of regression-based mapping at different points in a musical workflow: gesture design, sound design, and mapping design. These systems support expressive performance by creating gesture-timbre spaces that maximize sonic diversity while maintaining coherence, enabling reliable reproduction of target sounds as well as improvisatory exploration of a sonic corpus. They have been made available to the research community, and have been used by the authors in concert performance. © 2021, Springer Nature Switzerland AG.

关键词： Human computer interaction

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Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system

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Measurement: Sensors 2024年 33卷

作者： Alfred, Rayner Chinthamu, Narender Jayanthy, T. Muniyandy, Elangovan Dhiman, Tarun Kumar Nimmy John, T. Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia Forbes Executive Technology Council Member BostonMA United States Panimalar Institute of Technology Tamilnadu Chennai India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai602 105 India Graphic Era Hill University Dehradun R/S Graphic Era Deemed to Be University Dehradun India Department of Instrumentation Kerala Kochi-22 India

Our study set out to explore the integration of digital twins into manufacturing and production sectors with the aim of enhancing sustainability and operational efficiency. Employing a structured methodology, we utilized advanced 3D modeling techniques to create virtual representations of physical environments and products, forming the basis of our digital twin ecosystem. Through virtual product implementation, we simulated product behavior and performance, facilitating comprehensive testing and analysis before physical manufacturing. Leveraging artificial intelligence, particularly Support Vector Machine (SVM) optimization, we conducted performance analysis by processing data from interconnected sources, including social media platforms, to optimize digital transformation processes. Our results indicate that digital twins significantly enhance operational efficiency by detecting inefficiencies early, reducing production costs, and expediting time-to-market. Moreover, they contribute to sustainability efforts by minimizing resource wastage and environmental impact during manufacturing processes. The adoption of digital twins also fosters a culture of innovation and continuous improvement, enabling technological advancements and adaptability in corporate and industrial settings. In conclusion, our findings underscore the transformative potential of digital twins in driving positive change and optimizing industrial processes. © 2024 The Authors

关键词： Efficiency

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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Electrical and Computer Engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

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Vision Transformer for Contrastive Clustering

arXiv

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arXiv 2022年

作者： Ling, Hua-Bao Zhu, Bowen Huang, Dong Chen, Ding-Hua Wang, Chang-Dong Lai, Jian-Huang The College of Mathematics and Informatics South China Agricultural University Guangzhou China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation learning. Besides ViT, contrastive learning is another popular research topic recently. While previous contrastive learning works are mostly based on CNNs, some recent studies have attempted to combine ViT and contrastive learning for enhanced self-supervised learning. Despite the considerable progress, these combinations of ViT and contrastive learning mostly focus on the instance-level contrastiveness, which often overlook the global contrastiveness and also lack the ability to directly learn the clustering result (e.g., for images). In view of this, this paper presents a novel deep clustering approach termed Vision Transformer for Contrastive Clustering (VTCC), which for the first time, to our knowledge, unifies the Transformer and the contrastive learning for the image clustering task. Specifically, with two random augmentations performed on each image, we utilize a ViT encoder with two weight-sharing views as the backbone. To remedy the potential instability of the ViT, we incorporate a convolutional stem to split each augmented sample into a sequence of patches, which uses multiple stacked small convolutions instead of a big convolution in the patch projection layer. By learning the feature representations for the sequences of patches via the backbone, an instance projector and a cluster projector are further utilized to perform the instance-level contrastive learning and the global clustering structure learning, respectively. Experiments on eight image datasets demonstrate the stability (during the training-from-scratch) and the superiority (in clustering performance) of our VTCC approach over the state-of-the-art. © 2022, CC BY.

关键词： Convolution

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Answering Why? An Overview of Immersive Data Visualization Applications Using Multi-Level Typology of Visualization Task

Answering Why? An Overview of Immersive Data Visualization A...

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computing (ICOCO), IEEE International Conference on

作者： Najwa Ayuni Binti Jamaludin Farhan Bin Mohamed Mohd Shahrizal Bin Sunar Ali Bin Selamat Ondřej Krejcar School of Computing Faculty of Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Malaysia School of Computing Faculty of Engineering & Media and Game Innovation Center of Excellence (MaGICX) Universiti Teknologi Malaysia (UTM) Johor Bahru Malaysia Malaysia-Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia (UTM) Kuala Lumpur Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove Czech Republic

ISBN: (纸本)9781665489973

Immersive Analytics (IA) is a fast-growing research field that concerns improving and facilitating human sensemaking and data understanding through an immersive experience. Understanding the suitable application scenario that will benefit from IA enables a shift towards developing effective and meaningful applications. This paper aims to explore tasks and scenarios that can benefit from IA by conducting a systematic review of existing studies and mapping them according to the multi-level typology for abstract visualization tasks, which is also known as the What-Why-How framework. The study synthesized several works to answer the Why within the context of multiple levels of specificity. Finally, the limitations and future works are discussed.

关键词： Systematics Bibliographies Data visualization Immersive experience Task analysis

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