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2020 international conference on data processing algorithms and models, icdpam 2020

The proceedings contain 71 papers. The topics discussed include: the belt and road initiative: challenges and opportunities in tackling emerging infectious diseases;occurrence and molecular characterization of enterocytozoon bieneusi among long-tailed macaques (macaca fascicularis) in hainan province: high genetic diversity and zoonotic potential;TRPM8 is overexpressed in the respiratory tract of steroid-naive asthma patients;global health in tropical medicine: developed at the Center for international Health Research and exported to the world;and Helen Keller international and its contribution to elimination of neglected tropical diseases.

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international conference on data processing algorithms and models

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Journal of Physics: conference Series 2021年第1期1774卷 011001-011001页

作者： Zhou, Yanling Xiao, Xiaonan Li, Fei Hamburg University of Applied Sciences Germany Xiamen University China Chengdu University of Technology China Hefei University China

We were glad to introduce you that the 2020 international conference on data processing algorithms and models (icdpam 2020), held on 4th-6th, December, 2020, Xiamen, China. This conference was sponsored by Xiamen University, Chengdu University of Technology and Hamburg University of Applied Sciences. Affected by the COVID-19 pandemic and the implementation of the Chinese government’s isolation policy, many authors were unable to arrive at the Xiamen conference safely and on time, this year attendances were 48% lower than forecast, but in this special situation, some authors still come to Xiamen to attend the conference. The aim of icdpam is to present the latest research and results of scientists (professors, students, PhD Students, engineers, and post-doc scientist) related to data processing algorithms and models topics. This conference provides opportunities for the different areas delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration. The conference will be held every year to make it an ideal platform for people to share views and experiences in data processing algorithms and models and related areas. We were greatly honored to have invited Prof. Xiaonan Xiao to serve as our conference Chairman. He is a professor, Ph.D. advisor, and the chair of Department of Information and Computational Science at Xiamen University Tan Kah Kee College. He is the associate dean of the College of Information Science and Technology and a member of the international Association for Biostatistics & international Statistics Association. During the conference, the conference model was divided into three sessions, including keynote speeches, oral presentations, and Q&A discussion. In the first part, keynote speakers were each allocated 30-45 minutes to hold their speeches including 5min for Q&A. Then in the second part, some scholars, whose submissions were selected as

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12th international conference on data Science, Technology and Applications, data 2023

12th International Conference on Data Science, Technology an...

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12th international conference on data Science, Technology and Applications, data 2023

ISBN: (纸本)9789897586644

The proceedings contain 74 papers. The special focus in this conference is on data Science, Technology and Applications. The topics include: Temporal Multidimensional Model for Evolving Graph-Based data Warehouses;predicting Academic Performance of Low-Income Students in Public Ecuadorian Online Universities: An Educational data Mining Approach;identifying High-Quality Training data for Misinformation Detection∗;hawkes Processes on Social and Mass Media: A Causal Study of the #BlackLivesMatter Movement in the Summer of 2020;Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth;disease Prediction with Heterogeneous Graph of Electronic Health Records and Toxicogenomics data;heterogeneous Ensemble Learning for Modelling Species Distribution: A Case Study of Redstarts Habitat Suitability;a Proactive Approach for the Sustainable Management of Water Distribution Systems;Rigor in Applied data Science Research Based on DSR: A Literature Review;Extracting Frequent Gradual Patterns Based on SAT;An Advanced BERT LayerSum Model for Sentiment Classification of COVID-19 Tweets;a Comparison Study for Disaster Tweet Classification Using Deep Learning models;Prediction of QT Prolongation in Advanced Breast Cancer Patients Using Survival Modelling algorithms;anomaly Detection of Medical IoT Traffic Using Machine Learning;Semantic, Technical and Legal Interoperability of European Company Open data in Practice: The STIRdata Approach;analyzing Cyber-Physical Systems in Cars: A Case Study;structuring the End of the data Life Cycle;fundus Unimodal and Late Fusion Multimodal Diabetic Retinopathy Grading;a Visual Analysis of Hazardous Events in Contract Risk Management;pump and Dump Cryptocurrency Detection Using Social Media;Embedding-Enhanced Similarity Metrics for Next POI Recommendation.

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Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big data, APWeb-WAIM 2020 in conjunction with 3rd international Workshop on Knowledge Graph Management and Applications, KGMA 2020, 2nd international Workshop on Semi-structured Big data Management and Applications, SemiBDMA 2020 and 1st international Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2020

Asia Pacific Web and Web-Age Information Management Joint In...

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Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big data, APWeb-WAIM 2020 in conjunction with 3rd international Workshop on Knowledge Graph Management and Applications, KGMA 2020, 2nd international Workshop on Semi-structured Big data Management and Applications, SemiBDMA 2020 and 1st international Workshop on Deep Learning in Large-scale Unstructured data Analytics, DeepLUDA 2020

ISBN: (纸本)9789811604782

The proceedings contain 13 papers. The special focus in this conference is on Web and Big data. The topics include: Knowledge-Driven Multi-dimensional Dialogue Rewriting Model;towards Knowledge Graphs Federations: Issues and Technologies;a Learning Interests Oriented Model for Cold Start Recommendation;a Composite Chain Structure Blockchain Storage Method Based on Blockchain Technology;curriculum-Oriented Multi-goal Agent for Adaptive Learning;preface;neighborhood Query processing and Surrounding Objects Retrieval in Spatial databases: Applications and algorithms;distributed Storage and Query for Domain Knowledge Graphs;label Propagation Algorithm Based on Topological Potential;LBNet: A Model for Judicial Reading Comprehension;deep Semantic Hashing for Large-Scale Image Retrieval.

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international conference on Computational Methods and data Engineering, ICMDE 2020

International Conference on Computational Methods and Data E...

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international conference on Computational Methods and data Engineering, ICMDE 2020

ISBN: (纸本)9789811568756

The proceedings contain 50 papers. The special focus in this conference is on Computational Methods and data Engineering. The topics include: On roman domination of graphs using a genetic algorithm;general variable neighborhood search for the minimum stretch spanning tree problem;tabu-embedded simulated annealing algorithm for profile minimization problem;deep learning-based asset prognostics;evaluation of two feature extraction techniques for age-invariant face recognition;XGBoost: 2D-object recognition using shape descriptors and extreme gradient boosting classifier;Comparison of principle component analysis and stacked autoencoder on NSL-KDD dataset;maintainability configuration for component-based systems using fuzzy approach;development of petri net-based design model for energy efficiency in wireless sensor networks;Hybrid ANFIS-GA and ANFIS-PSO based models for prediction of type 2 diabetes mellitus;lifting wavelet and discrete cosine transform-based super-resolution for satellite image fusion;biologically inspired intelligent machine and its correlation to free will;weather status prediction of Dhaka City using machine learning;image processing: What, how and future;a study of efficient methods for selecting quasi-identifier for privacy-preserving data mining;day-ahead wind power forecasting using machine learning algorithms;query relational databases in Punjabi language;machine learning algorithms for big data analytics;fault classification using support vectors for unmanned helicopters;EEG signal analysis and emotion classification using bispectrum;social network analysis of youtube: A case study on content diversity and genre recommendation;Slack feedback analyzer (SFbA);a review of tools and techniques for preprocessing of textual data;A U-shaped printed UWB antenna with three band rejection;model for predicting academic performance through artificial intelligence.

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22nd international conference on data Analytics and Management in data Intensive Domains, DAMDID/RCDL 2020

22nd International Conference on Data Analytics and Manageme...

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22nd international conference on data Analytics and Management in data Intensive Domains, DAMDID/RCDL 2020

ISBN: (纸本)9783030811990

The proceedings contain 16 papers. The special focus in this conference is on data Analytics and Management in data Intensive Domains. The topics include: Part of Speech and Gramset Tagging algorithms for Unknown Words Based on Morphological Dictionaries of the Veps and Karelian Languages;extrinsic Evaluation of Cross-Lingual Embeddings on the Patent Classification Task;an Approach to Extracting Ontology Concepts from Requirements;data Driven Detection of Technological Trajectories;comparison of Cross-Lingual Similar Documents Retrieval Methods;algebraic models for Big data and Knowledge Management;a Cloud-Native Serverless Approach for Implementation of Batch Extract-Load Processes in data Lakes;pragmatic Interoperability and Translation of Industrial Engineering Problems into Modelling and Simulation Solutions;analysis of the Semantic Distance of Words in the RuWordNet Thesaurus;A Transformation of the RDF Mapping Language into a High-Level data Analysis Language for Execution in a Distributed Computing Environment;EMG and EEG Pattern Analysis for Monitoring Human Cognitive Activity during Emotional Stimulation;Finding the TMS-Targeted Group of Fibers Reconstructed from Diffusion MRI data;data for Binary Stars from Gaia DR2;preface.

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Affinity Propagation in Semi-Supervised Segmentation: A Biomedical Application

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第10期54卷 6023-6032页

作者： Chakraborty, Shouvik Mali, Kalyani Mitra, Sushmita Womens Polytech Dept Comp Sci & Technol Chandernagore 712136 India Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 India Indian Stat Inst Kolkata Machine Intelligence Unit Kolkata 700108 India

Given the scarcity of sufficient annotated data, using small sets of labeled samples under semi-supervision in biomedical imaging becomes necessary. Despite being highly successful, deep learning algorithms demand plenty of data to obtain significant performance. Complex data models make the usage of these methods costly. Selecting the correct model and tuning the hyperparameters of a model are also difficult jobs. Hence, a novel approach namely affinity propagation-based semi-supervised segmentation (APSS) is proposed. Here, affinity propagation clustering is modified and integrated with the advanced learning techniques that can efficiently use limited training data by discarding the completely exploited labeled data points. Moreover, a novel affinity calculation method is proposed considering both the Euclidean and geodesic distances to compute the distance between the two points on the histogram. This twofold contribution is tested using the three standard datasets (the international Skin Imaging Collaboration (ISIC) dermoscopic image dataset, the retinal fundus image dataset, and the liver tumor segmentation (LiTS) dataset). Results are compared with the three standard semi-supervised algorithms and four supervised algorithms. The effectiveness of the APSS approach in finding and exploiting the relationship between the labeled and unlabeled datasets is demonstrated in terms of qualitative (subjective evaluation and visual inspection) and quantitative performance (objective evaluation and numerical measurements).

关键词： Histograms Image segmentation Smoothing methods Kernel Biomedical imaging Estimation data models Biomedical image processing clustering methods image segmentation

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PVITNET:AN EFFECTIVE APPROACH FOR ANDROID MALWARE DETECTION USING PYRAMID FEATURE processing AND VISION TRANSFORMER 49

PVITNET:AN EFFECTIVE APPROACH FOR ANDROID MALWARE DETECTION ...

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49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yang, Denghui Ding, Yifan Zhang, Hao Li, Yizhou Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China Sichuan Univ Key Lab Data Protect & Intelligent Management Minist Educ Chengdu Peoples R China

ISBN: (纸本)9798350344868;9798350344851

This presents a significant challenge for detecting and combating malicious software. Users often grant software permissions unknowingly, exposing their devices to risks such as unauthorized access, file manipulation, and malware propagation. Traditional detection algorithms relying on limited permission-based strategies fall short in addressing this issue. To overcome this, we propose PVitNet (Network based On Pyramid Feature processing and Vision Transformer), an Android malware detection method. PVitNet incorporates pyramid feature processing, attention mechanisms, and an automatic feature extraction tool. By leveraging semantic information from feature pyramid models and learning shared characteristics among similar software, we successfully identify Android malware families. Our experiments on the CICMalDroid 2020 dataset demonstrate the effectiveness of our approach, with a 14.96% increase in accuracy and an F1 score of 98.31%.

关键词： pyramid feature processing vision transformer detection

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7th Annual international conference on Information Management and Big data, SIMBig 2020

7th Annual International Conference on Information Managemen...

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7th Annual international conference on Information Management and Big data, SIMBig 2020

ISBN: (纸本)9783030762278

The proceedings contain 39 papers. The special focus in this conference is on Information Management and Big data. The topics include: Modeling and Predicting the Lima Stock Exchange General Index with Bayesian Networks and Information from Foreign Markets;Comparative Study of Spatial Prediction models for Estimating PM2.5 Concentration Level in Urban Areas;prediction of Solar Radiation Using Neural Networks Forecasting;COVID-19 Infection Prediction and Classification;towards a Benchmark for Sedimentary Facies Classification: Applied to the Netherlands F3 Block;mobile Application for Movement Recognition in the Rehabilitation of the Anterior Cruciate Ligament of the Knee;semantic Segmentation Using Convolutional Neural Networks for Volume Estimation of Native Potatoes at High Speed;symbiotic Trackers’ Ensemble with Trackers’ Re-initialization for Face Tracking;symbiotic Trackers’ Ensemble with Trackers’ Re-initialization for Face Tracking;peruvian Citizens Reaction to Reactiva Perú Program: A Twitter Sentiment Analysis Approach;static Summarization Using Pearson’s Coefficient and Transfer Learning for Anomaly Detection for Surveillance Videos;humpback Whale’s Flukes Segmentation algorithms;improving Context-Aware Music Recommender Systems with a Dual Recurrent Neural Network;classification of Cybercrime Indicators in Open Social data;StrCoBSP: Relationship Strength-Aware Community-Based Social Profiling;identifying Differentiating Factors for Cyberbullying in Vine and Instagram;effect of Social algorithms on Media Source Publishers in Social Media Ecosystems;the Identification of Framing Language in Business Leaders’ Speech from the Mass Media;Clustering Analysis of Website Usage on Twitter During the COVID-19 Pandemic;calibrated Viewability Prediction for Premium Inventory Expansion;twitter Early Prediction of Preferences and Tendencies Based in Neighborhood Behavior;distributed Identity Management for Semantic Entities.

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A strategy for network multi-layer information fusion based on multimodel in user emotional polarity analysis

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international Journal of Cognitive Computing in Engineering 2025年第1期6卷 120-130页

作者： Wang, Ronghua Zhuang, Peng School of Journalism & Communication Xinxiang University Xinxiang453003 China Sociology Department Buffalo State University BuffaloNY14222 United States

Emotional analysis is an important research direction in natural language processing, which aims to automatically recognize and understand emotions and emotional polarity in texts. The study employed multiple emotional analysis models to analyze emotions in text data. Then the emotional analysis results of different models were integrated to improve accuracy through a hierarchical information fusion strategy. Meanwhile, graph embedding algorithms such as DeepWalk and Node2vec were utilized to process node information in graph data. The embedding representation of graph nodes was utilized for emotional analysis of nodes. In addition, based on models such as graph convolutional neural networks for message passing, contextual information of nodes was obtained to improve emotional analysis performance. These results indicated that the proportion of passive users was higher than that of active users, at 57.14 % and 39.68 %, respectively. Despite the large number of negative users, the frequency of connections between active users was significantly higher than between negative users. Multiple algorithms were utilized for emotional analysis and prediction. The AUC curve performed well, but the accuracy was only 56.69 %, which needs improvement. The study also evaluated the root mean square error of network predictions, with errors typically below 0.2 in large networks, demonstrating relatively accurate prediction results. This guides the further development of emotional analysis technology, promoting the research and improvement of more accurate and reliable emotional analysis methods. © 2024

关键词： data accuracy

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