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Auxiliary Information Regularized machine for Multiple Modality Feature learning 24

Auxiliary Information Regularized Machine for Multiple Modal...

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Yang, Yang Ye, Han-Jia Zhan, De-Chuan Jiang, Yuan Nanjing Univ Natl Key Lab Novel Software Technol Collaborat Innovat Ctr Novel Software Technol & I Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781577357384

In real world applications, data are often with multiple modalities. Previous works assumed that each modality contains sufficient information for target and can be treated with equal importance. However, it is often that different modalities are of various importance in real tasks, e.g., the facial feature is weak modality and the fingerprint feature is strong modality in ID recognition. In this paper, we point out that different modalities should be treated with different strategies and propose the Auxiliary information Regularized machine (ARM), which works by extracting the most discriminative feature subspace of weak modality while regularizing the strong modal predictor. Experiments on binary and multi-class datasets demonstrate the advantages of our proposed approach ARM.

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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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1st international Conference on Applied Intelligence and Informatics, AII 2021

1st International Conference on Applied Intelligence and Inf...

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1st international Conference on Applied Intelligence and Informatics, AII 2021

ISBN: (纸本)9783030822682

The proceedings contain 30 papers. The special focus in this conference is on Applied Intelligence and Informatics. The topics include: Key Techniques and Challenges for Processing of Heart Sound Signals;enhanced Signal Processing Using Modified Cyclic Shift Tree Denoising;a machine learning Driven Android Based Mobile Application for Flower Identification;a Generative Text Summarization Model Based on Document structure Neural Network;human Gender Detection from Facial Images Using Convolution Neural Network;few-Shot learning for Tamil Handwritten Character recognition Using Deep Siamese Convolutional Neural Network;A CNN Based Model for Venomous and Non-venomous Snake Classification;recognition of Dysfluency in Speech: A Bidirectional Long-Short Term Memory Based Approach;distributed Denial of Service Attack Detection Using machine learning and Class Oversampling;glaucoma Detection Using Inception Convolutional Neural Network V3;scientific Metrological Analysis of Government Services Based on Big data Analysis and Visualization Software Driven by Information Technology;violent Video Event Detection: A Local Optimal Oriented pattern Based Approach;human Age Estimation Using Deep learning from Gait data;an Error Resilient Video Transmission in Ad Hoc Network Using Error Diffusion Block Truncation Coding;ALO: AI for Least Observed People;COVID-Hero: machine learning Based COVID-19 Awareness Enhancement Mobile Game for Children;Literature Classification Model of Deep learning Based on BERT-BiLstM——Taking COVID-19 as an Example;identifying Relevant stakeholders in Digital Healthcare;COVID-19 Detection Using Chest X-Ray Images with a RegNet structured Deep learning Model;mixed Bangla-English Spoken Digit Classification Using Convolutional Neural Network;iConDet: An Intelligent Portable Healthcare App for the Detection of Conjunctivitis.

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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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machine learning Techniques for Intrusion Detection: A Comparative Analysis 16

Machine Learning Techniques for Intrusion Detection: A Compa...

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1st international Conference on Informatics and Analytics (ICIA)

作者： Hamid, Yasir Sugumaran, M. Journaux, Ludovic Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry India Univ Burgundy Lab Le2i Dijon France

ISBN: (纸本)9781450347563

With the growth of internet world has transformed into a global market with all monetary and business exercises being carried online. Being the most imperative resource of the developing scene, it is the vulnerable object and hence needs to be secured from the users with dangerous personality set. Since the Internet does not have focal surveillance component, assailants once in a while, utilizing varied and advancing hacking topologies discover a path to bypass framework's security and one such collection of assaults is Intrusion. An intrusion is a movement of breaking into the framework by compromising the security arrangements of the framework set up. The technique of looking at the system information for the conceivable intrusions is known intrusion detection. For the last two decades, automatic intrusion detection system has been an important exploration point. Till now researchers have developed Intrusion Detection Systems (IDS) with the capability of detecting attacks in several available environments;latest on the scene are machine learning approaches. machine learning techniques are the set of evolving algorithms that learn with experience, have improved performance in the situations they have already encountered and also enjoy a broad range of applications in speech recognition, pattern detection, outlier analysis etc. There are a number of machine learning techniques developed for different applications and there is no universal technique that can work equally well on all datasets. In this work, we evaluate all the machine learning algorithms provided by Weka against the standard data set for intrusion detection i.e. KddCupp99. Different measurements contemplated are False Positive Rate, precision, ROC, True Positive Rate.

关键词： False Positive IDS machine learning Precision ROC True Positive

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1st edition of the international Conference on Artificial Intelligence and Green Computing, ICAIGC 2023

1st edition of the International Conference on Artificial In...

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1st edition of the international Conference on Artificial Intelligence and Green Computing, ICAIGC 2023

ISBN: (纸本)9783031465833

The proceedings contain 22 papers. The special focus in this conference is on Artificial Intelligence and Green Computing. The topics include: Maximization of Lifetime in Wireless Sensor Networks Using pattern Search Algorithm;task Deadline-Based Computation Offloading Algorithm for Service Time Minimization in Mobile Edge Computing;detection of Web-Based Attacks using Tree-Based learning Models: An Evaluation study;energy Minimization in Wireless Sensor Networks Based Bio-Inspired Algorithms;Heuristic Optimization of the LEACH Routing Protocol in Wireless Sensor Networks;toward Understanding the Impact of Demographic Factors on Cybersecurity Awareness in the Moroccan Context;health data Security in a Big data Environment;Handling the ED Problem Using a DTSA in Smart Grids;Multivariate Time Series data Prediction Based on Social Sentiments Community and LstM Method (S-S-LstM);longitudinal study of the Thyroid Surgery Effect Based on Computer Vision;improving Wheat Yield Estimating by Using Satellites data and machine learning—Deep learning Algorithm-In Morocco;online Process mining: A Systematic Literature Review;machine learning with Nighttime Lights to Predict Morocco’s Gross Domestic Product;pectoral Muscle Segmentation Using Mammogram Images in Medio Lateral Oblique View;a Nearest Neighbor-Based Hamiltonian Clustering Algorithm;an Acoustic Analysis of Voice Before and After Thyroidectomy;estimation of Water Turbidity by Image-Based learning Approaches;an Image Compression Approach Based on Convolutional AutoEncoder;offline Writer Identification Based on Diagonal Gradient Angle of Small Fragments;Enhanced Aircraft Time Delay Prediction Using Weighted Hybrid ML and Dimensionality Reduction.

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1st IFIP TC 5, WG 8.4, 8.9, 12.9 international Cross-Domain Conference on machine learning and Knowledge Extraction, CD-MAKE 2017

1st IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain ...

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1st IFIP TC 5, WG 8.4, 8.9, 12.9 international Cross-Domain Conference on machine learning and Knowledge Extraction, CD-MAKE 2017

ISBN: (纸本)9783319668079

The proceedings contain 24 papers. The special focus in this conference is on machine learning and Knowledge Extraction. The topics include: On distance mapping from non-euclidean spaces to euclidean spaces;some remarks on the algebraic properties of group invariant operators in persistent homology;decentralized computation of homology in wireless sensor networks using spanning trees;detecting and ranking API usage pattern in large source code repository: a LFM based approach;towards a framework for assistance systems to support work processes in smart factories;online self-disclosure: from users’ regrets to instructional awareness;decision tree rule induction for detecting covert timing channels in TCP/IP traffic;practical estimation of mutual information on non-euclidean spaces;DO NOT DIstURB? classifier behavior on perturbed datasets;a short-term forecast approach of public buildings’ power demands upon multi-source data;on the challenges and opportunities in visualization for machine learning and knowledge extraction: a research agenda;quantitative externalization of visual data analysis results using local regression models;analysis of online user behaviour for art and culture events;on joint representation learning of network structure and document content;ambient assisted living technologies from the perspectives of older people and professionals;human activity recognition using recurrent neural networks;modeling golf player skill using machine learning;a declarative semantics for P2P systems and towards the automatic detection of nutritional incompatibilities based on recipe titles.

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Comparative Analysis of Time Series and machine learning Models for Air Quality Prediction Utilizing IoT data 13th

Comparative Analysis of Time Series and Machine Learning M...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Vonitsanos, Gerasimos Panagiotakopoulos, Theodor Kameas, Achilles Computer Engineering and Informatics Department University of Patras Patras Greece School of Technology and Science Hellenic Open University Patras Greece School of Business University of Nicosia Nicosia Cyprus

ISBN: (纸本)9783031632266

Air pollution has been shown to have serious negative effects on people’s health, the environment, and the economy. It is becoming more and more crucial to model, predict, and monitor air quality, particularly in urban areas. Air quality prediction is challenging because of the dynamic nature, instability, and high spatial and temporal variability of particles and pollutants. Internet of things technologies and machine learning offer an efficient way to address these challenges and enables the implementation of effective air quality prediction models. This paper aims to provide a comparative analysis of time series and machine learning methods for air quality prediction based on data collected through IoT sensors. These methods have been evaluated for PM10, PM2.5, and Air Quality Index (AQI) particles. The results indicate that while deep learning models (LstM) perform better for the air quality index, ARIMA and SVM algorithms best predict the concentrations of the researched air pollutants (PM2.5, PM10). © IFIP international Federation for Information Processing 2024.

关键词： Air quality

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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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Designing smart environments: A paradigm based on learning and prediction

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1st international Conference on pattern recognition and machine Intelligence, PReMI 2005

作者： Das, Sajal K. Cook, Diane J. Department of Computer Science and Engineering University of Texas Arlington

ISBN: (纸本)3540305068

We propose a learning and prediction based paradigm for designing smart home environments. The foundation of this paradigm lies in information theory as it manages uncertainties of the inhabitants' contexts (e.g., locations or activities) in daily lives. The idea is to build compressed dictionaries of context-aware data collected from sensors and devices monitoring and/or controlling the smart environment, efficiently learn from these profiles, and finally predict inhabitant's future contexts. Successful prediction helps automate device control operations and tasks within the environment as well as to identify anomalies. Thus, the learning and prediction based paradigm optimizes such goal functions of smart environments as minimizing maintenance cost, manual interactions and energy utilization. After identifying important features of smart environments, we present an overview of our MavHome architecture and apply the proposed paradigm to the inhabitant's location and activity tracking and prediction, and automated decision-making capability. © Springer-Verlag Berlin Heidelberg 2005.

关键词： learning systems

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