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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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A Single-Stage Deep Learning Approach for Multiple Treatment and Diagnosis in Panoramic X-ray 23th

A Single-Stage Deep Learning Approach for Multiple Treatment...

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23rd International Conference on Intelligent Systems Design and applications, ISDA 2023

作者： Singh, Nripendra Kumar Faisal, Mohammad Hasan, Shamimul Goswami, Gaurav Raza, Khalid Department of Computer Science Jamia Millia Islamia New Delhi India Department of Computer Application Galgotias College of Engineering and Technology Greater Noida India Faculty of Dentistry Jamia Millia Islamia New Delhi India Deep Learning Research Team Synergy Labs Gurugram India

ISBN: (纸本)9783031648120

This work investigates the recognition of multiple dental treatment and diagnosis conditions in a full scan dental panoramic image. In this study, we proposed a single-stage oriented deep learning model for five dental therapies, namely restoration, root canal treatment (RCT), RCT with crown, dental implant (DI), and DI with crown, two dental diagnoses (impacted tooth and tooth bud), and natural teeth without any treatment as tooth class. However, considering all eight classes to achieve a wide range of dental radiology data interpretation results in a highly imbalanced dataset. We considered a custom dataset of 661 dental panoramic images where a frequent category is tooth counted 11,282 times, while the least presence category is DI with the crown having 75 instances. The performance of the proposed work was evaluated with average precision of each treatment and diagnosis class. The result of RCT from the dental treatment category achieved an average precision score of 82.63% and recognition of impacted teeth with an average precision score of 18.78% from the dental diagnosis categories. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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A Design and Implementation of M Tyoe Systems wnhcih uses ICT technology in Grids

A Design and Implementation of M Tyoe Systems wnhcih uses IC...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jangid, Satish Kumar Singh, Alka Pushparajesh, V. Vivekananda Global University Department of Electrical Engineering Jaipur India Noida Institute of Engineering & Technology Computer Application Uttar Pradesh Greater Noida India Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India

ISBN: (纸本)9798350357769

In the context of smart grids, multi-agent-based applications have garnered a lot of interest recently. Concurrently, integration of IT (information and communication technology) has emerged as a crucial element of the infrastructure of smart grids. This research attempts to give a comprehensive assessment of the technological framework and literature pertaining to the utilization of Multi-Agent Societies (MAS) and the addition of information and communication technologies in electrical power system deployments. In the environment of artificially intelligent grids, agents are thought of as sentient beings that can make decisions and behave in a dynamic and autonomous way by drawing on their innate intellect and past experiences. ICT also makes data and information transmission easier, which is a game-changer in the transition from conventional to smart grids. This article summarizes the multi-agent concept in smart grids and offers a thorough literature study on related topics to elucidate their applications. Additionally, it pays particular attention to Cti standards, particularly IEC 61850, which integrates MAS with ICT. The last section of the paper introduces laboratory framework concepts, which provide insight on the actual use of IEC 61850. © 2023 IEEE.

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Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention 3

Prediction of rainfall-type debris flow in Jiangjiagou based...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Gao, Yuan Li, Yingna Kunming University of Science and Technology College of Faculty of Information Engineering and Automation Kunming China Key Laboratory of Computer Technology Application of Yunnan Province Kunming China

ISBN: (纸本)9781665459112

Through the neural network model, the rainfall-type debris flow in Jiangjiagou is predicted. Based on seven meteorological factors with time series characteristics, such as daily rainfall, previous effective rainfall, ground temperature, and humidity, which have been screened by features, the Jiangjiagou rainfall debris flow prediction and early warning model based on LSTM-Attention is constructed. The results show that: (1) LSTM-Attention model is 0.02 higher than that of the Attention-LSTM model, and the prediction effect is better. (2) The prediction accuracy of LSTM-Attention model for Jiangjiagou rainfall-type debris flow can reach 0.88, which is 0.05, 0.07, 0.04, 0.25, 0.13 higher than that of LSTM model, RNN model, RNN-Attention model, SVM model, and BP model, respectively. Research results: It provides a new idea and method for the prediction and research of rainfall-type debris flow, which can be referenced by scholars. © 2022 IEEE.

关键词： Long short-term memory

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Integrated Intelligent Computing Models for Cognitive-Based Neurological Disease Interpretation in Children: A Survey

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EAI Endorsed Transactions on Pervasive Health and technology 2024年 10卷

作者： Tandon, Archana Mazumdar, Bireshwar Dass Pal, Manoj Kumar Faculty of Computer Application United University Prayagraj India School of Computer Science Engineering and Technology Bennett University U.P Greater Noida India Faculty of Computer Science & Engineering United University Prayagraj India

INTRODUCTION: This piece of work provides the description of integrated intelligent computing models for the interpretation of cognitive-based neurological diseases in children. These diseases can have a significant impact on children's cognitive and developmental functioning. OBJECTIVES: The research work review the current diagnosis and treatment methods for cognitive based neurological diseases and discusses the potential of machine learning, deep learning, Natural language processing, speech recognition, brain imaging, and signal processing techniques in interpreting the diseases. METHODS: A survey of recent research on integrated intelligent computing models for cognitive-based neurological disease interpretation in children is presented, highlighting the benefits and limitations of these models. RESULTS: The significant of this work provide important implications for healthcare practice and policy, with strengthen diagnosis and treatment of cognitive-based neurological diseases in children. CONCLUSION: This research paper concludes with a discussion of the ethical and legal considerations surrounding the use of intelligent computing models in healthcare, as well as future research directions in this area. © 2024 A. Tandon et al.

关键词： Speech recognition

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Transformative applications and Key Challenges of Generative AI 7

Transformative Applications and Key Challenges of Generative...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kovari, Attila Katona, Jozsef Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary University of Dunaújváros Department of Institute of Engineering Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI. © 2024 IEEE.

关键词： Generative adversarial networks

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Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit

Research on intrusion detection algorithm based on dilated c...

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2022 International Conference on Algorithms, Microchips and Network applications

作者： Liu, Xingyuan Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Laboratory of Yunnan Province Kunming650500 China

ISBN: (纸本)9781510653290

Intrusion detection based on machine learning plays an important role in the security protection of the network environment. In order to improve the accuracy of network intrusion detection, intrusion detection algorithm based on dilated convolution and a two-way gating unit is proposed. Firstly, dilated convolution (DCNN) is used to increase the receptive field of information and extract spatial features. Secondly, the bidirectional gated cyclic unit (bigru) model is used to capture the temporal features of data, and the attention mechanism is introduced to increase the calculation of feature importance. Compared with the classical machine learning classifier, this model has a high detection rate. The multi-classification experiments on the famous UNSW-NB15 data set show that the model has leading performance, and its classification accuracy can reach 97.14%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Intrusion detection

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The present situation and shifts observed in wetlands within the *** Seaway region of Canada,utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine

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Big Earth Data 2025年第1期9卷 47-71页

作者： Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh Digital Environmental Group WSP Canada Inc.OttawaONCanada Department of Computer Science Chalmers University of TechnologyGothenburgSweden Digital Environmental Group WSP Canada Inc.EdmontonABCanada dIndependent Researcher Department of Photogrammetry and Remote Sensing Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina

This study examined wetland trends in the *** Seaway(~500,000 km^(2))in Canada over the past four *** this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were *** samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged *** spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to ***,a change analysis was conducted to calculate the loss and gain of different wetland ***,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,*** was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 ***,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an ***,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),*** study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.

关键词： Remote sensing Google Earth Engine(GEE) cloud computing satellite change detection continuous change detection and classification(CCDC) wetlands

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