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arXiv 2024年

作者： Xiong, Jiaqi Yin, Nan Liang, Shiyang Li, Haoyang Wang, Yingxu Ai, Duo Pan, Fang Wang, Jingjie Department of Gastroenterology Tangdu Hospital Fourth Military Medical University Shaanxi 710038 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou528225 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Internal Medicine The No. 944 Hospital of Joint Logistic Support Force of PLA Xiongguan Road Jiu Quan735000 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Dermatology Xijing Hospital Fourth Military Medical University No 127 of West Changle Road Shaanxi Xi’an710032 China

Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not considered the skewed degree distribution of genes. Specifically, some genes may regulate multiple target genes while some genes may be regulated by multiple regulator genes. Such a skewed degree distribution issue significantly complicates the application of directed graph embedding methods. To tackle this issue, we propose the Cross-Attention Complex Dual Graph Embedding Model (XATGRN). Our XATGRN employs a cross-attention mechanism to effectively capture intricate gene interactions from gene expression profiles. Additionally, it uses a Dual Complex Graph Embedding approach to manage the skewed degree distribution, thereby ensuring precise prediction of regulatory relationships and their directionality. Our model consistently outperforms existing state-of-the-art methods across various datasets, underscoring its efficacy in elucidating complex gene regulatory mechanisms. Our codes used in this paper are publicly available at: https://***/kikixiong/XATGRN. © 2024, CC BY.

关键词： Graph embeddings

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

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Procedia Computer science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

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Application of Machine Learning Techniques to Ad-Hoc Wireless Networks Performance Enhancement

Application of Machine Learning Techniques to Ad-Hoc Wireles...

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International Conference on Computing and Networking technology (ICCNT)

作者： Parikshit N Mahalle V.J. Vijayalakshmi Vivek Saraswat T Vinithra Banu Dinesh Goyal Batani Raghavendra Rao Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Quantum University Research Center Quantum University School of Mangement - PG Management JAIN(Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Advert-hoc Wi-Fi networks are characterized via decentralized verbal exchange topology and lack of any bodily infrastructure. This has made them a compelling choice in many applications which include civilian and army operations, emergency offerings, and healthcare. However, because of their decentralized nature they are prone to issues such as network congestion, frequent protocol packet drops, and vulnerable sign propagation. to triumph over those challenges, gadget gaining knowledge of strategies can be utilized that may each improve present protocol overall performance and aid in the improvement of novel routing *** of the software of device getting to know techniques is within the prediction of packet drop charge in advert-hoc wireless networks. An online or actual-time prediction of packet drop rate can be used to dynamically alter routing protocols, supplying better provider great and robustness to the network. For example, methods the use of deep getting to know together with recurrent neural networks may be trained the use of historic information series to appropriately predict the expected packet-drops within the community. This data can then be used to alter the routing protocols and/or lessen hyperlink congestion. Another instance where machine studying strategies come into play is within the improvement of novel routing protocols. Existing protocols may be used as enter statistics, and the device getting to know algorithms may be used to optimize them, study the underlying traits.

关键词： Performance evaluation Wireless networks Software algorithms Throughput Routing Routing protocols Ad hoc networks Software Topology Wireless fidelity

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Remote Sensing Time Series Analysis:A Review of data and Applications

Journal of Remote Sensing

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Journal of Remote Sensing 2024年第1期4卷 34-66页

作者： Yingchun Fu Zhe Zhu Liangyun Liu Wenfeng Zhan Tao He Huanfeng Shen Jun Zhao Yongxue Liu Hongsheng Zhang Zihan Liu Yufei Xue Zurui Ao School of Geography South China Normal UniversityGuangzhouGuangdong 510631China Department of Natural Resources and the Environment University of ConnecticutStorrsCT 06269USA Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjingJiangsu 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application NanjingJiangsu 210023China Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere School of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430079China School of Resource and Environmental Sciences Wuhan UniversityWuhanHubei 430079China School of Marine Sciences Sun Yat-sen UniversityZhuhaiGuangdong 519082China School of Geography and Ocean Science Nanjing UniversityNanjingJiangsu 210023China Department of Geography The University of Hong KongPokfulamHong KongChina School of Artificial Intelligence Anhui UniversityHefeiAnhui 230601China Beidou Research Institute Faculty of EngineeringSouth China Normal UniversityGuangzhou 510631China

Remote sensing time series research and applications are advancing rapidly in land,ocean,and atmosphere science,demonstrating emerging capabilities in space-based monitoring methodologies and diverse application *** prompts a comprehensive review of remote sensing time series observations,time series data reconstruction,derived products,and the current progress,challenges,and future directions in their *** high-frequency new data,i.e.,a constellation strategy,increasing computing power and advancing deep learning algorithms,are driving a paradigm shift from traditional point-in-time mapping to near-real-time monitoring tasks,and even to modeling integration of parameter inversion and prediction in land,water,and air ***,the 3 main projects,namely,the Global Climate Observing System,the United States Geological Survey/National Aeronautics and Space Administration(USGS/NASA)Landsat science team,and the China Global Land Surface Satellite(GLASS)team,along with other time series-derived products,have found widespread applications in the research of Earth’s radiation balance and human-land *** have also been utilized for tasks such as land use change detection,assessing coastal effects,ocean environment monitoring,and supporting carbon neutrality ***,the 3 critical challenges and future directions were highlighted including multimode time series data fusion,deep learning modeling for task-specific domain adaptation,and fine-scale remote sensing applications by using dense time *** review distills historical and current developments spanning the last several decades,providing an insightful understanding into the advancements in remote sensing time series data and applications.

关键词： time series analysis ocean science constellation strategyincreasing computing power land science derived products data reconstruction remote sensing series data reconstructionderived

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Multiple Target Research of Construction Progress on the Basis of on BIM and Decide Tree Algorithms

Multiple Target Research of Construction Progress on the Bas...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： S. Selvakanmani E. Manigandan C. Hazarathaiah Yadav Satheesh Kumar S Brijesh Singh B. Karthik Department of Information Technology RMK Engineering College RSM Nagar Chennai Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Chennai Tamilnadu India Department of Chemistry Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Sathyamangalam Tamilnadu India Department of MBA CMR Institute Of Technology Bangalore India Department of Electrical and Electronics Engineering Sona College of Technology Salem India

Because of India's growing urbanization, coupled with dwindling urban land supply and rising land costs, the construction of high-rise structures has emerged as the dominant method to urban planning and development. The rapid growth of India's economy and the rapid pace of urbanization have resulted in an increasing urban population, raising the requirement for effective land usage. High-rise structures have gradually been the main center of urban construction projects in response to these *** primary goal of this study is to look into the use of Building Information Modeling (BIM) in building projects with the goal of optimizing the use of information received from it. Numerous graphs and algorithmic formulas are developed during the study to examine and illustrate these features. According to the findings of the study, a significant amount, accounting for 28.2% of the total, is related to various focused research procedures. As a result, the interest and significance of this field of investigation are expected to endure and develop in the coming years..

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Customer review sentiment analysis using SVC, KNN and Vader’s sentiment analysis

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AIP Conference Proceedings 2025年第1期3255卷

作者： Ajay Talele Madhuri Barhate Siddhartha Chakrabarty Sneha Jain Sneha Bhat Somrath Bisen Department of Multidisciplinary Engineering Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

This research explores the application of Natural Language Processing (NLP) for analyzing customer review sentiment analysis, where NLP methods aim to extract valuable insights from text data. The process involves gathering and preparing the data, identifying key features, and training a sentimental analysis model for accurate classification. This model forms the backbone of a user-friendly frontend interface, designed for seamless interaction with the analysis result. Users can input text or review documents, which are then processed through the trained model to generate precise sentiment scores. To enhance understanding, visualizations such as charts, graphs, and word clouds are incorporated. These visuals help users to see how sentiments are changing over time and what the main topics or feelings in the data are. Ultimately, this integration approach enables businesses to use sentiment analysis to make smarter decisions and make customers happier. By understanding, how customers feel more deeply, companies can adjust their plans to better meet their needs, resulting in happier customers and better business results.

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Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach

arXiv

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arXiv 2024年

作者： Liu, Xian-Xian Xu, Mingkun Wei, Yuanyuan Qin, Huafeng Song, Qun Fong, Simon Tien, Feng Luo, Wei Gao, Juntao Zhang, Zhihua Siu, Shirley The Department of Computer and Information Science University of Macau 999078 China Guangdong Institute of Intelligence Science and Technology Zhuhai519031 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 China Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China Institute of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China The director of the Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou510060 China Tsinghua University California Beijing100084 China Beijing Institute of Genomics Chinese Academy of Sciences Beijing100101 China Centre for Artificial Intelligence Driven Drug Discovery Macao Polytechnic University Rua de Luís Gonzaga Gomes 999078 China

Timely and precise classification and segmentation of gastric bleeding in endoscopic imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is critical in life-saving medical procedures. Traditional methods grapple with the challenge posed by the indistinguishable intensity values of bleeding tissues adjacent to other gastric structures. Our study seeks to revolutionize this domain by introducing a novel deep learning model, the Dual Spatial Kernelized Constrained Fuzzy C-Means (Deep DuS-KFCM) clustering algorithm. This Hybrid Neuro-Fuzzy system synergizes Neural Networks with Fuzzy Logic to offer a highly precise and efficient identification of bleeding regions. Implementing a two-fold coarse-to-fine strategy for segmentation, this model initially employs the Spatial Kernelized Fuzzy C-Means (SKFCM) algorithm enhanced with spatial intensity profiles and subsequently harnesses the state-of-the-art DeepLabv3+ with ResNet50 architecture to refine the segmentation output. Through extensive experiments across mainstream gastric bleeding and red spots datasets, our Deep DuS-KFCM model demonstrated unprecedented accuracy rates of 87.95%, coupled with a specificity of 96.33%, outperforming contemporary segmentation methods. The findings underscore the model’s robustness against noise and its outstanding segmentation capabilities, particularly for identifying subtle bleeding symptoms, thereby presenting a significant leap forward in medical image processing. © 2024, CC BY.

关键词： Medical image processing

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Procedia Computer science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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DiM-Gestor: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2

arXiv

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arXiv 2024年

作者： Zhang, Fan Zhao, Siyuan Ji, Naye Wang, Zhaohan Wu, Jingmei Gao, Fuxing Ye, Zhenqing Yan, Leyao Dai, Lanxin Geng, Weidong Lyu, Xin Zhao, Bozuo Yu, Dingguo Du, Hui Hu, Bin School of Media Engineering Communication University of Zhejiang China School of Broadcast Announcing Arts Communication University of Zhejiang China Faculty of Humanities and Arts Macau University of Science and Technology China School of Animation and Digital Arts Communication University of China Beijing China College of Computer Science and Technology Zhejiang University The Research Center for Artificial Intelligence and Fine Arts Zhejiang Lab Zhejiang China Changjiang Academy of Art and Design Shantou University China

Speech-driven gesture generation using transformer-based generative models represents a rapidly advancing area within virtual human creation. However, existing models face significant challenges due to their quadratic time and space complexities, limiting scalability and efficiency. To address these limitations, we introduce DiM-Gestor, an innovative end-to-end generative model leveraging the Mamba-2 architecture. DiM-Gestor features a dual-component framework: (1) a fuzzy feature extractor and (2) a speech-to-gesture mapping module, both built on the Mamba-2. The fuzzy feature extractor, integrated with a Chinese Pre-trained Model and Mamba-2, autonomously extracts implicit, continuous speech features. These features are synthesized into a unified latent representation and then processed by the speech-to-gesture mapping module. This module employs an Adaptive Layer Normalization (AdaLN)-enhanced Mamba-2 mechanism to uniformly apply transformations across all sequence tokens. This enables precise modeling of the nuanced interplay between speech features and gesture dynamics. We utilize a diffusion model to train and infer diverse gesture outputs. Extensive subjective and objective evaluations conducted on the newly released Chinese Co-Speech Gestures dataset corroborate the efficacy of our proposed model. Compared with Transformer-based architecture, the assessments reveal that our approach delivers competitive results and significantly reduces memory usage—approximately 2.4 times—and enhances inference speeds by 2 to 4 times. Additionally, we released the CCG dataset, a Chinese Co-Speech Gestures dataset, comprising 15.97 hours (six styles across five scenarios) of 3D full-body skeleton gesture motion performed by professional Chinese TV broadcasters. © 2024, CC BY.

关键词： Mapping

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Mathematical Model Validation of Search Protocols in MP2P Networks

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Computers, Materials & Continua 2021年第8期68卷 1807-1829页

作者： Ajay Arunachalam Vinayakumar Ravi Moez Krichen Roobaea Alroobaea Saeed Rubaiee Centre for Applied Autonomous Sensor Systems(AASS) Örebro UniversityÖrebroSweden Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Faculty of CSIT Al-Baha UniversitySaudi Arabia ReDCAD LaboratoryUniversity of SfaxTunisia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Industrial and Systems Engineering College of EngineeringUniversity of JeddahJeddahSaudi Arabia

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission *** is a type of broadcast where the received packet is retransmitted once by every *** naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search *** of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried *** earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired ***,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over *** this paper,we describe how P2P resource discovery protocols perform badly over *** address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured *** provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search ***,we also derive three relevant search performance metrics,i.e.,mean *** steps needed to find a resource,the probability of finding a resource,and the mean *** message *** validated the analytical expressions through *** simulation results closely matched with our analyticalmodel,justifying our *** proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

关键词： Search protocols random walk MANET P2P networks P2P MANET mathematical model peer-to-peer wireless ad hoc network flooding mobile P2P unstructured P2P NS-2 network simulator

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