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Artificial Intelligence Applications and Innovations 1

引用

丛书名： IFIP Advances in Information and Communication Technology

1000年

作者： Lazaros Iliadis Ilias Maglogiannis Harris Papadopoulos

ISBN: (数字)9783642239601

ISBN: (纸本)9783642239595;9783642269653

The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th international Conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 international Conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. The second volume includes the papers that were accepted for presentation at the AIAI 2011 conference. They are organized in topical sections on computer vision and robotics, classification/pattern recognition, financial and management applications of AI, fuzzy systems, learning and novel algorithms, recurrent and radial basis function ANN, machine learning, generic algorithms, data mining, reinforcement learning, Web applications of ANN, medical applications of ANN and ethics of AI, and environmental and earth applications of AI. The volume also contains the accepted papers from the First workshop on Computational Intelligence in Software Engineering (CISE 2011) and the workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2011).

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Information storage and Retrieval database Management data mining and Knowledge Discovery Computation by Abstract Devices

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Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and machine learning 15

Detecting Factors Responsible for Diabetes Prevalence in Nig...

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15th Int Conf on Network and Serv Management (CNSM) / 1st Int workshop on Analyt for Serv and Application Management (AnServApp) / Int workshop on High-Precision Networks Operat and Control, Segment Routing and Serv Function Chaining (HiPNet+SR/SFC)

作者： Oyebode, Oladapo Orji, Rita Dalhousie Univ Fac Comp Sci Halifax NS Canada

ISBN: (纸本)9783903176249

Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country's diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms;hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.

关键词： Diabetes Prevalence factors Social media Text mining machine learning Intervention design

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Conceptual analysis of big data using ontologies and EER 1st

Conceptual analysis of big data using ontologies and EER

引用

1st international workshop on machine learning, Optimization, and Big data, MOD 2015

作者： Jitkajornwanich, Kulsawasd Elmasri, Ramez Ministry of Science and Technology of Thailand 120 The Government Complex Chaeng Wattana Road Lak Si Bangkok10210 Thailand Department of Computer Science and Engineering The University of Texas at Arlington 701 S Nedderman Dr ArlingtonTX76019 United States

ISBN: (纸本)9783319279251

Large amounts of "big data" are generated every day, many in a "raw" format that is difficult to analyze and mine. This data contains potential hidden meaningful concepts, but much of the data is superfluous and not of interest to the domain experts. Thus, dealing with big raw data solely by applying a set of distributed computing technologies (e.g., MapReduce, BSP [Bulk Synchronous Parallel], and Spark) and/or distributed storage systems, namely NoSQL, is generally not sufficient. Extracting the full knowledge that is hidden in the raw data is necessary to efficiently enable analysis and mining. The data needs to be processed to remove the superfluous parts and generate the meaningful domainspecific concepts. In this paper, we propose a framework that incorporates conceptual modeling and EER principle to effectively extract conceptual knowl‐ edge from the raw data so that mining and analysis can be applied to the extracted conceptual data. © Springer international Publishing Switzerland 2015.

关键词： Big data

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Knowledge Graph data Enrichment Based on a Software Library for Text Mapping to the Sustainable Development Goals 2

Knowledge Graph Data Enrichment Based on a Software Library ...

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Joint of the 2nd international workshop on Knowledge Graph Generation From Text and the 1st international BiKE Challenge, TEXT2KG 2023 and BiKE 2023

作者： Mandilara, Ioanna Fotopoulou, Eleni Androna, Christina Maria Zafeiropoulos, Anastasios Papavassiliou, Symeon Institute of Communication and Computer Systems National Technical University of Athens Athens Greece

Over the last few years, there has been a significant increase in the release of massive amounts of data related to the Sustainable Development Goals (SDGs). This has been driven by the recognition that data is critical for monitoring progress towards the SDGs, identifying areas that require attention, and informing policy decisions. Given that a significant percentage of the provided information is made available in documents, the availability of software libraries that can enable scientists to easily extract information regarding the importance given to the SDGs in the documents’ text is considered crucial. Towards this direction, we have developed an open-source Python software library that provides the mapping between text and the SDGs, taking advantage of novel machine learning techniques. The provided software library is modular and permits the dynamic selection of trained machine learning models for analysis purposes. The outcomes from the usage of the software library are fed for data population of a knowledge graph that is targeted to the tracking of information around the SDGs. The overall approach is open and can be easily adopted by scientists and policy makers to support participatory modeling processes, as well as participatory decision making and action planning for the development of solutions for climate-resilient regions. © 2023 CEUR-WS. All rights reserved.

关键词： Sustainable development

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Pruning Extreme Wavelets learning machine by Automatic Relevance Determination 20th

Pruning Extreme Wavelets Learning Machine by Automatic Relev...

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20th international Conference on Engineering Applications of Neural Networks (EANN) /1st international workshop on Pervasive Intelligence (PEINT)

作者： de Campos Souza, Paulo V. Silva Araujo, Vinicius J. Araujo, Vanessa S. Batista, Lucas O. Guimaraes, Augusto J. CEFET MG Ave Amazonas5253Nova Suica Belo Horizonte MG Brazil Fac UNA Betim Ave Gov Valadares640 Ctr Betim MG Brazil

ISBN: (纸本)9783030202576;9783030202569

Extreme learning machines are used for various contexts in artificial intelligence, such as for classifying patterns, performing time series prediction and regression problems, and being a more viable solution for training hidden layer weights to determine values of the learning model. However, the essence, the model determines that these weights should be determined randomly, and the Moore Penrose pseudoinverse will define only the weights that will act in the output layer. Random weights make this learning a black box because there is no relationship between the hidden layer weights and the problem data. This paper proposes the initialization of weights and bias in the hidden layer through the Wavelets transform that allows the two parameters, previously initialized at random, to be more representative about the problem domain, allowing the frequency range of the input patterns of the network to aid in the definition of weights of the ELM hidden layer. To assist in the representativeness of the data, a technique of selection of characteristics based on automatic relevance determination will be applied to the selection of the most characteristic dimensions of the problem. To compose the network structure, activation functions of the type rectified linear unit, also called ReLU, were used. The proposed model was submitted to the classification test of binary patterns in real classes, and the results show that the proposition of this work assists in bringing better accuracy to the classification results, and thus can be considered a feasible proposition to the training of neural networks that use extreme learning machine.

关键词： Extreme learning machine Wavelets ReLU Automatic relevance determination pattern classification

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Web Usage mining for Determining a Website’s Usage pattern: A Case study of Government Website 1st

Web Usage Mining for Determining a Website’s Usage Patter...

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1st international Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Rawira, Panunsiya Esichaikul, Vatcharaporn Asian Institute of Technology Pathum Thani12120 Thailand

ISBN: (纸本)9789819979684

Web usage mining is a crucial research area that aims to uncover user behavior patterns from web log data because web usage mining can be used to analyze a website’s usage. This study examined web usage mining to discover online users’ usage patterns and used the results to redesign and improve the government website. This study aims to help online customers obtain a better experience. A dataset was collected from the Metropolitan Electricity Authority (MEA) website. Various algorithms, including association rule mining (Apriori and Frequent pattern-Growth (FP-Growth)) and sequential pattern mining (Generalized Sequential pattern (GSP) and Prefix-Span), were used to mine the user usage data. The first 30 frequently visited patterns of web usage mining results were selected for analysis and to develop a prototype. It revealed that most pages were accessed directly. Moreover, most users were interested in alternative energy information, the Fuel Adjustment Charge (at the given time) or the FT rate, the power outage announcement page, and reducing electric expenses information. Furthermore, the analysis indicated that customers were interested in making online processes, such as using the contact-us feature, downloading forms, and calculating their electric expenses. A survey was conducted with 30 participants and the results were compared with the usage data from the weblog. Welch’s t-test was utilized to evaluate the prototype. The findings indicated that the newly implemented website, based on the user usage patterns, was more effective and reduced the user’s usage time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： machine learning

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Efficient Large Scale Medical Image dataset Preparation for machine learning Applications 1

引用

1st MICCAI international workshop on data Engineering in Medical Imaging (DEMI)

作者： Denner, stefan Scherer, Jonas Kades, Klaus Bounias, Dimitrios Schader, Philipp Kausch, Lisa Bujotzek, Markus Bucher, Andreas Michael Penzkofer, Tobias Maier-Hein, Klaus German Canc Res Ctr Div Med Image Comp Heidelberg Germany Heidelberg Univ Med Fac Heidelberg Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Natl Ctr Tumor Dis NCT Heidelberg Germany Goethe Univ Frankfurt Univ Hosp Inst Diagnost & Intervent Radiol Frankfurt Germany Charite Univ Med Berlin Dept Radiol Berlin Germany

ISBN: (数字)9783031449925

ISBN: (纸本)9783031449918;9783031449925

In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be facilitated in machine learning algorithms. This paper introduces an innovative data curation tool, developed as part of the Kaapana (https://***/kaapana/ kaapana) open-source toolkit, aimed at streamlining the organization, management, and processing of large-scale medical imaging datasets. The tool is specifically tailored to meet the needs of radiologists and machine learning researchers. It incorporates advanced search, auto-annotation and efficient tagging functionalities for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is essential for developing robust machine learning models. Furthermore, Kaapana is integrated within the Radiological Cooperative Network (RACOON), a pioneering initiative aimed at creating a comprehensive national infrastructure for the aggregation, transmission, and consolidation of radiological data across all university clinics throughout Germany. A supplementary video showcasing the tool's functionalities can be accessed at https://***/MICCAI- DEMI2023.

关键词： Medical Imaging data Curation machine learning Kaapana dataset Preperation Quality Control Bias Detection

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1st international Conference on Soft Computing in data Science, SCDS 2015

1st International Conference on Soft Computing in Data Scien...

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1st international Conference on Soft Computing in data Science, SCDS 2015

ISBN: (纸本)9789812879356

The proceedings contain 25 papers. The special focus in this conference is on data mining and Fuzzy Computing. The topics include: An improved particle swarm optimization via velocity-based reinitialization for feature selection;classifying forum questions using PCA and machine learning for improving online CQA;data projection effects in frequent itemsets mining;data quality issues in data migration;reviewing classification approaches in sentiment analysis;possibility vague soft expert set theory and its application in decision making;an iterative method for solving fuzzy fractional differential equations;contrast comparison of flat electroencephalography image;an autocatalytic model of a pressurized water reactor in a nuclear power generation;selfish gene image segmentation algorithm;solving curriculum based course timetabling by hybridizing local search based method within harmony search algorithm;a parallel latent semantic indexing (LSI) algorithm for malay hadith translated document retrieval;short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine;implementation of dynamic traffic routing for traffic congestion;a comparative study of video coding standard performance via local area network;partial differential equation (PDE) based image smoothing system for digital radiographic image;main structure of handwritten jawi sub-word representation using numeric code;evaluating the usability of homestay websites in Malaysia using automated tools;humanoid-robot intervention for children with autism and accuracy assessment of urban growth pattern classification methods using confusion matrix and roc analysis.

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1st international Conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

1st International Conference on Computational Intelligence f...

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1st international Conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

ISBN: (纸本)9789811984921

The proceedings contain 68 papers. The special focus in this conference is on Computational Intelligence for Engineering and Management Applications. The topics include: strokes-Related Disease Prediction Using machine learning Classifiers and Deep Belief Network Model;analysis and Detection of Fraudulence Using machine learning Practices in Healthcare Using Digital Twin;prediction and Analysis of Polycystic Ovary Syndrome Using machine learning;feature Selection for Medical Diagnosis Using machine learning: A Review;convolutional Neural Network Architectures Comparison for X-Ray Image Classification for Disease Identification;Secure Shift-Invariant ED Mask-Based Encrypted Medical Image Watermarking;fusion-Based Feature Extraction Technique Using Representation learning for Content-Based Image Classification;a Comparative study on Challenges and Solutions on Hand Gesture recognition;design a Computer-Aided Diagnosis System to Find Out Tumor Portion in Mammogram Image with Classification Technique;a Novel Soft-Computing Technique in Hydroxyapatite Coating Selection for Orthopedic Prosthesis;performance Analysis of Panoramic Dental X-Ray Images Using Discrete Wavelet Transform and Unbiased Risk Estimation;efficient Image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features;texture and Deep Feature Extraction in Brain Tumor Segmentation Using Hybrid Ensemble Classifier;A Systematic Review on Sentiment Analysis for the Depression Detection During COVID-19 Pandemic;vehicular Adhoc Networks: A Review;the data Vortex Switch Architectures—A Review;survey on Genomic Prediction in Biomedical Using Artificial Intelligence;a Brief Review on Right to Recall Voting System Based on Performance Using machine learning and Blockchain Technology;sentiment Analysis Techniques: A Review;network Traffic Classification Techniques: A Review;remote Production Monitoring System.

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The study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm 1st

The Study of the Compatibility Rules of Traditional Chinese ...

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1st international workshop on data Management and Analytics for Medicine and Healthcare (DMAH) / workshop on Big-Graphs Online Querying (Big-O (Q))

作者： Wang, Miao Li, Jiayun Chen, Li Huang, Yanjun Zhou, Qiang Che, Lijuan Shang, Huiliang Shanghai Univ TCM 1200 Cailun Rd Shanghai 201210 Peoples R China Fudan Univ Dept Elect Engn 220 Handan Rd Shanghai 200433 Peoples R China

ISBN: (纸本)9783319415765;9783319415758

One of the major research contents carried by scholars of Traditional Chinese medical science (TCM) is to discover the compatibility rules of herbs to increase the efficacy in treating certain syndromes. However, up to now, most of the compatibility rules of herbs are based on empirical analyses, which make them hard to study. Since concepts of Big data and machine learning have been popularized gradually, how to use data mining techniques to effectively figure out core herbs and compatibility rules becomes the main research aspect of TCM informatics. In this paper, the hypergraph partitioning algorithm HMETIS based on Apriori is applied to exploit and analyze clinical data about lung cancer. The result shows that all 15 Chinese herbs obtained by the algorithm accord with the core concepts of the treatment of lung cancer by experienced TCM doctors, namely replenishing nutrition, clearing heat-toxin, resolving phlegm and eliminating pathogenic factors.

关键词： data mining Compatibility rules of herbs Apriori algorithm Hypergraph Community partitioning HMETIS

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