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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Gan Zhan Xiuju Du Jing Liu Yinhao Li Lanfen Lin Jingsong Li Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Spatial transcriptomics (ST) offers insights into gene expression patterns within tumor microenvironments, but its widespread application is impeded by cost constraints. To address this, predicting ST from Histology emerges as a cost-effective alternative. However, current methods such as STNet, HistoGene, and Hist2ST exhibit limitations, either overlooking stain variation across datasets or failing to well explore inter-spot correlations in scenarios with limited Whole Slide Image (WSI) data. In response, we present STFormer, a deep learning approach that incorporates the Style-Aug module to enhance feature generalization through medically-irrelevant style transfer augmentation. Additionally, the Cross-WSI Transformer module is introduced to capture Cross-WSI spot relationships efficiently. Our experimental results, conducted on both internal and external datasets, demonstrate that STFormer surpasses existing methods by a substantial margin. This showcases its potential as a powerful tool for spatial transcriptomics predictions, addressing critical gaps in current methodologies.

关键词： Deep learning Costs Correlation Histopathology Transcriptomics Colorectal cancer Transformers Gene expression Tumors Biomedical imaging

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks

arXiv

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arXiv 2023年

作者： Cai, Heng Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University China School of Computer Science and Engineering Southeast University China School of Data and Computer Science Shandong Woman’s University China

Medical image segmentation typically necessitates a large and precisely annotated dataset. However, obtaining pixel-wise annotation is a labor-intensive task that requires significant effort from domain experts, making it challenging to obtain in practical clinical scenarios. In such situations, reducing the amount of annotation required is a more practical approach. One feasible direction is sparse annotation, which involves annotating only a few slices, and has several advantages over traditional weak annotation methods such as bounding boxes and scribbles, as it preserves exact boundaries. However, learning from sparse annotation is challenging due to the scarcity of supervision signals. To address this issue, we propose a framework that can robustly learn from sparse annotation using the cross-teaching of both 3D and 2D networks. Considering the characteristic of these networks, we develop two pseudo label selection strategies, which are hard-soft confidence threshold and consistent label fusion. Our experimental results on the MMWHS dataset demonstrate that our method outperforms the state-of-the-art (SOTA) semi-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound result. Our code is available at https://***/HengCai-NJU/3D2DCT. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Improving Alzheimer's Disease Diagnosis on Brain MRI Scans with an Ensemble of Deep Learning Models

Improving Alzheimer's Disease Diagnosis on Brain MRI Scans w...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Md. Sazid Reza Mir Md. Jahangir Kabir Md. Abdur Rakib Mollah Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Data Science and Innovation TD School University of Technology Sydney Sydney Australia

Alzheimer's disease (AD) is a widespread neurolog-ical condition affecting millions globally. It gradually advances, leading to memory loss, cognitive deterioration, and a substantial decline in overall quality of life for those affected. AD patients experience memory decline, eroding cherished memories and straining relationships, while daily tasks become challenging. Numerous investigations have been conducted in this field, as the timely identification of Alzheimer's disease at its initial stage is of the utmost importance. A major limitation in this field is the predominant emphasis on using single fine-tuned CNN architecture or comparing pre-trained and custom CNN models for Alzheimer's detection, often on small datasets, which neglects a more comprehensive approach. Using smaller datasets can negatively impact deep learning modeling accuracy due to overfitting, limited representation, and poor generalization. This study addresses the current research problems and proposes an ensemble approach that combines predictions from various pre-trained models, including DenseNet-121, EfficientNet B7, ResNet-50, VGG-19, and also from a Custom CNN. The model averaging ensemble method was applied, a subset of the Stacking Ensemble, to two ADNI datasets, with dataset-I being the larger. The goal was to assess the efficacy of this ensemble approach for accurate multiclass classification on ADNI datasets, where it successfully identified all classes despite differing sample volumes. A vast experiment was conducted on two distinct and widely recognized real-world datasets, resulting in accuracies of 99.96% and 98.90% respectively. Finally, the outcome of the research compared with recent research findings demonstrates the potential of our approach in advancing Alzheimer's disease detection by outperforming other benchmark approaches by a significant margin.

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Video Frame Prediction Using Convolutional LSTM Encoder and Decoder

Video Frame Prediction Using Convolutional LSTM Encoder and ...

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data, Computation and Communication (ICDCC), International Conference on

作者： G. Revathy Pavithra Jagadeesan R. Vajubunnisa Begum Vijay Anand P H. Jasmin S. Gayathri Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering Karpaga vinayaga college of engineering and technology Chennai India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Teynampet Chennai Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798331532956

ISBN: (纸本)9798331532963

With rapid growth of audiovisual content now spreading through social networking sites, chances of exposure towards violent and mature content by the youth are growing. This project addresses the risk of detection of such content using advanced video frame prediction techniques. We present here an encoder-decoder model of Convolutional LSTMs in the film for violent and obscene sequence detection. The spatiotemporal nature of video data encourages our algorithm to exploit accurate predictions of future frames from past content, which can be used in real time for dangerous material detection. The study relies on recent literature that highlights the psychological implications arising from the viewing of violent content by teenagers, thus making the need for efficient automated detection mechanisms ever more pertinent. Furthermore, we present a complete definition of “violence” for the standardized detection mechanisms and to cross-compare various studies. Using Violent Scene Detection, our results indicate high improvements in the detection accuracy and therefore become effective in this context for parental control and content moderation. Such results help safeguard children in the increasingly digital environment: traditional as well as currently ever-increasing.

关键词： Accuracy Social networking (online) Psychology Machine learning Streaming media Prediction algorithms Real-time systems Spatiotemporal phenomena Decoding Long short term memory

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Multi-Scale Noise-Guided Progressive Network for Image Splicing Detection and Localization

SSRN

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SSRN 2024年

作者： Zhang, Dengyong Jiang, Ningjing Li, Feng Chen, Jiaxin Liao, Xin Yang, Gaobo Ding, Xiangling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha410114 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer and Communication Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China

Image splicing can easily be used in illegal activities, which may have negative impact on society. Therefore, detecting splicing images and precisely localizing tampered regions are challenging tasks. In this work, we propose a dual-branch Multi-Scale Noise-Guided Progressive Network (MSNP-Net). The multi-resolution branch extracts deep semantic features of images while suppressing redundant noise. A multi-scale noise-guided branch is designed to capture more subtle tampering artifacts and guide the network to strengthen the learning of spatial structure features. The two branches complement and restrict each other. The features of different scales are fused in a gradual mechanism, and the feature expression is aggregated through the spatial channel feature aggregation module (SCAM). A large number of experiments show that the MSNP-Net proposed in this paper has better completeness and lower false alarm rate for the detection and localization of spliced images. It can achieve more refined detection results, and has good stability and robustness. Significantly outperforms other state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Utilizing Artificial Intelligence for Efficient Resource Allocation and Logistics in Humanitarian Aid

Utilizing Artificial Intelligence for Efficient Resource All...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： B Rajalakshmi P. Aswini H Pal Thethi Himanshu Rai Goyal Jajimoggala Sravanthi Ali Abdulhussein Hameed Department of Computer Science N.H.C.E Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India LPU Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of CSE GRIET Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

It's possible that artificial intelligence (AI) will change the way crisis relief is managed and delivered by providing flexible solutions that use real-time data insights and adjust to new situations. AI programs, which use a lot of data analysis, demand forecasts, and predictive models, can help emergency response systems change how they distribute resources. These projects' main goals are to provide quick and effective assistance while also making the best use of resources and reducing response times. Machine learning algorithms are important for improving decision-making because they allow systems to handle unexpected situations well and adapt quickly and correctly to changing times. The usefulness of these methods depends on their ability to handle transportation issues, which speeds up and improves the supply of aid while cutting down on waste and inefficient operations. Wanting to make the most of AI-driven solutions stresses the importance of being creative and giving when using them. These solutions have the potential to change the way businesses and charities handle their resources. Using AI to help plan crisis aid ensures that vulnerable people get the right help when needed and makes the best use of available resources. To find and use successful strategies that can clearly lower pain and save lives, it is important to understand the promise of artificial intelligence in crisis aid.

关键词： Decision making Supply chains Transportation Reinforcement learning Real-time systems Time factors Resource management Artificial intelligence Predictive analytics Genetic algorithms

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Deep Learning-based Cluster Analysis for Healthcare data Classification

Deep Learning-based Cluster Analysis for Healthcare Data Cla...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M. Gayathri V. Kavitha Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India

The use of cutting-edge technology in the medical field results in the production of massive volumes of data on a daily basis. Various categories of information are applied in the domain of healthcare, including clinic information, medical record data, and genetic information. In addition, real-time monitoring in the medical industry produces enormous volumes of data, and properly interpreting such enormous amounts of data is a significant problem. The evaluation of medical information becomes more required so that suitable drugs may be supplied and issues can be avoided by taking appropriate measures depending on the history of the patient. Automation makes data analysis more effective, but performance might deteriorate when there are problems with data integrity, diversity, or consistency. Automation makes data analysis more effective. In order to handle the management of massive amounts of data, many models that are powered by neural networks have been developed; still researchers are currently attempting to develop a superior model that is more accurate. Hence, fuzzy c denotes the process of clustering, whereas generative adversarial networks are used in this study to clusters and classify medical data, aiming to achieve the maximum degree of efficiency in classification. The experiment will use both the benchmark lung cancer sample and the arrhythmias sample. The proposed model achieves a maximum accuracy of 97.3% for dataset 1 and 98.2% for dataset 2, outperforming other methods such as support vector machine, decision tree, and random forest algorithms.

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Investigation of Environmental Monitoring Based on Three IoT Wireless Sensors 2

Investigation of Environmental Monitoring Based on Three IoT...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Yamini, B. Arthy, M. Priya, S Geetha Kathiresan, S. Jeneetha Jebanazer, J. Uthayakumar, G.S. Srm Institute of Science and Technology Department of Networking and Communication Engineering Tamil Nadu Kattankulathur Chennai India Srm Institute of Science and Technology School of Computing Department of Data Science& Business Systems Tamil Nadu Kattankulathur Chennai India R. M. D Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Environmental monitoring has become more crucial as a result of the recent climatic changes, making it a hot issue and a very active study field. This field uses wireless sensor networks and remote sensing to collect data about the environment. Recent developments, including the Internet of Things (IoT) concept, the cloud computing paradigm, and cyber-physical systems, assist the administration and transmission of enormous volumes of data about changes in environmental parameters. The current work presents three different IoT-based wireless sensors for monitoring the environment and the surrounding area: one uses Wi-Fi User datagram Protocol (UDP)-based communication, one uses Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one uses Bluetooth Smart. The ability to capture data at distant places and see it from any device with an Internet connection is a feature shared by all of the systems that have been discussed. This makes it possible to monitor vast geographic regions. These systems' specific development details are discussed, along with their main distinctions and affinities. The three created systems were evaluated for their suitability for IoT-based solutions, taking into consideration their energy independence, usability, solution complexity, and Internet connectivity capability. © 2023 IEEE.

关键词： Bluetooth

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A Novel Dynamic Watermarking for Secure data Protection from Cyber Theft Based on Artificial Intelligence Supervision 8

A Novel Dynamic Watermarking for Secure Data Protection from...

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8th IEEE International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Madala, Ravikiran Monica, R. Malwade, Sulakshana Sagar Selvakumaran, S. Budhavale, Shilpa Jivan Sathyabalaji, N. University of Cumberlands Information Technology DurhamNC United States Nandha Engineering College Department of Electronics and Communication Engineering Tamilnadu Erode52 India Dr.Vishwanath Karad School of Polytechnic and Skill Development Department of Computer Engineering Maharashtra Pune India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Dr. Vishwanath Karad School of Polytechnic and Skill Development Department of Computer Engineering Maharashtra Pune India Jkk Munirajah College of Technology Department of Computer Science and Engineering Tamilnadu Erode India

ISBN: (纸本)9798350347791

One of the centre innovations of the Fourth Modern Insurgency (otherwise called Industry 4.0) is artificial intelligence (computer based intelligence), which can be utilized to shield Web associated gadgets from dangers, assaults, harm, and undesirable access. The current range of cyber security issues can be deftly handled by utilising well-known simulated intelligence techniques such as AI and profound learning procedures, the possibility of normal language handling, information portrayal and thinking, as well as the possibility of information or rule-based master frameworks displaying. In view of these man-made intelligence strategies, we give an exhaustive examination of "Simulated intelligence driven cyber security"in this article, which may be helpful for clever cyber security government agencies and the board. The cyber security registration method can be made more computerised and intelligent than the standard security frameworks by the security intelligence exhibiting in light of such simulated intelligence ***, we present various choices for future examination that fall inside the domain of our examination. At last, this paper's primary objective is to give direction and references to industry specialists in the field of cyber security, especially from a canny registering or simulated intelligence based specialized viewpoint. © 2023 IEEE.

关键词： Artificial intelligence

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