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检索条件"任意字段=18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017"

共 91 条记录，以下是1-10 订阅

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18th international conference on intelligent data engineering and automated learning, ideal 2017

18th International Conference on Intelligent Data Engineerin...

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18th international conference on intelligent data engineering and automated learning, ideal 2017

ISBN: (纸本)9783319689340

the proceedings contain 65 papers. the special focus in this conference is on intelligent data engineering and automated learning. the topics include: Cross-media retrieval of tourism big data based on deep features and topic semantics;information retrieval with implicitly temporal queries;on the relations of theoretical foundations of different causal inference algorithms;SibStCNN and TBCNN + kNN-TED: New models over tree structures for source code classification;a community detection algorithm based on local double rings and fireworks algorithm;cost sensitive matrix factorization for face recognition;Research of dengue fever prediction in san juan, puerto rico based on a KNN regression model;identification of nonlinear system based on complex-valued flexible neural network;research on the method of splitting large class diagram based on multilevel partitioning;dynamic community detection algorithm based on automatic parameter adjustment;ford motorcar identification from single-camera side-view image based on convolutional neural network;predicting personality traits of users in social networks;face anti-spoofing algorithm based on gray level co-occurrence matrix and dual tree complex wavelet transform;high-accuracy deep convolution neural network for image super-resolution;an improved density peak clustering algorithm;consensus-based parallel algorithm for robust convex optimization with scenario approach in colored network;heterogeneous context-aware recommendation algorithm with semi-supervised tensor factorization;object detection with proposals in high-resolution optical remote sensing images;towards spectral-texture approach to hyperspectral image analysis for plant classification;face attributes retrieval by multi-label contractive hashing;an ant colony random walk algorithm for overlapping community detection;trajectory similarity-based prediction with information fusion for remaining useful life.

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Low Consumption Models for Disease Diagnosis in Isolated Farms 25th

Low Consumption Models for Disease Diagnosis in Isolated Far...

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25th international conference on intelligent data engineering and automated learning

作者： Vazquez, I. X. Garcia-Vico, A. M. Seker, H. Sedano, J. ITCL Technol Ctr Burgos 09001 Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain Univ Sharjah Coll Comp & Informat Dept Informat Syst Sharjah U Arab Emirates

ISBN: (纸本)9783031777301;9783031777318

the detection of bacterial and viral microbes is pivotal for both human and animal well-being in public health services and veterinary care, but it traditionally requires time-consuming procedures and expert technicians. However, the rise of Machine learning and Deep learning has led to a surge in the application of new techniques that can perform bacterial and viral detection faster and at a lower cost. Yet, despite that success, Deep learning approaches tend to have high energy demands, which can in some contexts limit their application, increasing both costs and environmental concerns. In this study, a new hybrid methodology, in which an Artificial Neural Network was combined with a more energy efficient Spiking Neural Network, was employed to develop a model able to classify 18 species of Eimeria parasites, affecting both rabbits and chickens, from microscope images. We show how significant energy savings can be obtained from SNN layers, while their use in the model can improve its performance.

关键词： Eimeria Microorganisms Detection Green AI Spiking Neural Network

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Multi-Modal Deep learning Architecture for Accurate Driver Behavior Recognition in automated Driving Systems 18

Multi-Modal Deep Learning Architecture for Accurate Driver B...

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18th international conference on intelligent Systems and Knowledge engineering, ISKE 2023

作者： Alhaek, Fares Liang, Weichao Rajeh, Taha M. Javed, Muhammad Hafeez Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9798350318401

automated driving systems and advanced driver assistance systems require accurate and reliable driver behavior recognition models to ensure safe and efficient operation. the complexity of background contents in real-world driving scenarios presents a significant challenge. Moreover, the distinction between different driving behaviors can be extremely subtle. Minor variations in body movements, gestures, or vehicle control, which are challenging to discern accurately, may indicate distinct behaviors. To address the above issues, this paper proposes a novel multi-model deep learning architecture (MMDLA) for driver behavior recognition by integrating multiple modalities, including RGB frames, depth information, and skeleton data, to provide a more comprehensive representation of driver behavior. the proposed model consists of two stages. In the first stage, we use local and global feature extraction to extract features from RGB and Depth frames. Additionally, we construct a graph from 3D skeleton data and feed it to a graph convolutional network to extract its features. In the second stage, we perform ensemble learning and fuse the diversity of multiple features together. Experiments on the Drive&Act dataset shows that our model achieves superior performance, outperforming existing models on both hierarchical and fine-grained activity recognition tasks. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Blockchain and Deep learning-Based Decentralized Anomaly Detection Framework for VANET 18

Blockchain and Deep Learning-Based Decentralized Anomaly Det...

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18th IEEE international conference on Vehicular Electronics and Safety, ICVES 2024

作者： Jetani, Harshil Patel, Janam Mahida, Nikunjkumar Patel, Man Gupta, Rajesh Tanwar, Sudeep Gupta, Ankur Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Model Institute of Engineering and Technology Department of Computer Science and Engineering J and K India

ISBN: (纸本)9798350387278

the automotive industry is increasing exponentially day by day providing a safer and efficient experience to the consumers. intelligent Transportation Systems (ITS) has become more crucial with the increasing number of vehicles. Moreover, there is a growing demand for intelligent services like efficient traffic monitoring in vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication environments. Vehicular Ad-hoc Network (VANET) represents the demanding and vibrant environment for securing the interaction between the vehicle, RSUs, and pedestrians through the Controller area network (CAN). RF jamming Attacks can be integrated through wireless mediums. the paper presents an improvement in securing VANETs through the combination of Blockchain technology (BT) with Deep learning (DL). In DL-based model, RNN is utilized for detecting and classifying malicious activities. BT ensures secure transactions and automated trust-less interactions using smart contracts. thus, the non-attack data classified by DL layer is communicated using BT. IPFS is used for storing the information to enhance the scalability while mitigating the maintenance cost of BT. the accuracy achieved by the RNN model during evaluation was 97%. Multiple models were evaluated over metrics like accuracy, precision, recall, F1-score, and ROC-AUC curve. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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A Probabilistic Approach: Querying Web Resources in the Presence of Uncertainty 24th

A Probabilistic Approach: Querying Web Resources in the Pr...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Omri, Asma Bensliamne, Djamal Omri, Mohamed Nazih MARS Research Laboratory University of Sousse Sousse Tunisia University Claude Bernard Lyon 1 LIRIS Research Laboratory Villeurbanne France

ISBN: (纸本)9783031482311

Uncertainty in data naturally arises in various applications, such as data integration and Web information extraction. A few examples are the following. When information from different sources is conflicting, inconsistent, or simply presented in incompatible forms the result of integrating these sources necessarily involves uncertainty as to which fact is correct or which is the best mapping to a global schema. data uncertainty is often ignored, or modeled in a specific, per-application manner. this may be an unsatisfying solution in the long run, especially when the uncertainty needs to be retained throughout complex and potentially imprecise processing of the data. In this paper, we study the basic activities of web resources that are affected by uncertainty, more specifically, modeling, programming and evaluation. We propose a probabilistic approach that treats uncertainty in all these activities. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： data integration

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Function Execution Log Based Judgment System for Arduino learning Practice 18th

Function Execution Log Based Judgment System for Arduino Lea...

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18th international conference on intelligent Tutoring Systems (ITS)

作者： Seo, Kangbok Lee, Woojin Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9783031096808;9783031096792

the automatic judgment system, which has been widely used in recent programming lectures, is being developed mainly in a language that is performed based on a console. Embedded systems such as Arduino require more effort from instructors to guide students, but research on systems that support them is slow. In order to support this, this paper intends to support instructors and students by developing an Arduino practice judgment system based on a function execution log. the Arduino practice judgment system can perform Arduino practice judgment by processing the Fritzing output and the source code execution result in the form of a function execution log rather than the actual Arduino HW. In addition, it was shown that the system can be applied to actual lectures to make lectures more efficient.

关键词： automated assessment Programming education automated feedback Arduino practice

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QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction 22nd

QG-SKI: Question Classification and MCQ Question Generation ...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Dhanvardini, R. Deepak, Gerard Santhanavijayan, A. Health Care Informatics Domain Optum-United Health Groups Hyderabad India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

E-learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. the utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. this paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. the proposed QG-SKI model uses LOD Cloud and Wiki data to generate Ontologies dynamically and Knowledge reservoir is performed. the dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. these algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. the suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Differentially Distributed Private Intelligence Security in Cybersecurity Infrastructures 5

Differentially Distributed Private Intelligence Security in ...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Jenifer, P. Geebin, A.R. Brundha, P. Manohar, E. Francis Xavier Engineering College Department of Cse Tirunelveli India

ISBN: (纸本)9781665474672

Novel ways of security against attacks to critical infrastructure are becoming more important as their number and sophistication increase. In order to provide the kind of security that is necessary, one must consider both preventive measures and an understanding of the current state of affairs inside the systems being protected. Researches and institutions have brought security solutions that are both intelligent and selfsufficient, such as the necessity for sophisticated proactive monitoring capability in Intrusion Detection Systems (IDS). this study argues that critical infrastructures need automated IDS solutions and that adding Machine learning (ML) approaches into IDS will be useful for developing dynamically adjustable algorithms that can promptly respond to changing circumstances and threats. To this end, this study assesses the several degrees of automation provided by the IDS and provide a plan for introducing preemptive automation into critical settings. Finally, this study evaluates how well the proposed method stacks up against other methods previously presented in the literature. Machine learning presents difficulties in the field of cybersecurity that must be effectively addressed using sound methodology and theory. Transfer learning, support vector machines, and Bayesian classification are just a few examples of the statistical and machine learning approaches that have been shown to be useful in reducing the effects of cyberattacks. It is crucial to establish smart security systems that can discover hidden patterns and insights from packet headers and then construct a data-driven machine-learning model to avoid these assaults. this study examines the use of machine learning methods to cybersecurity data with the goal of bolstering the safety of existing infrastructures. this article has addressed the current state of cybersecurity threats as well as the deployment of machine learning methods to combat the aforementioned challenges. © 2023 IEEE.

关键词： Cybersecurity

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Deep learning semantic segmentation for indoor terrain extraction: Toward better informing free-living wearable gait assessment

Deep learning semantic segmentation for indoor terrain extra...

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IEEE-EMBS international conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS international conference on Biomedical and Health Informatics (BHI)

作者： Moore, Jason Stuart, Sam Walker, Richard McMeekin, Peter Young, Fraser Godfrey, Alan Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Northumbria Univ Dept Sport Exercise & Rehabil Newcastle Upon Tyne Tyne & Wear England Northumbria Healthcare NHS Fdn Trust Newcastle Upon Tyne Tyne & Wear England Northumbria Univ Dept Nursing Midwifery & Hlth Newcastle Upon Tyne Tyne & Wear England

ISBN: (数字)9781665459259

ISBN: (纸本)9781665459259

Contemporary approaches to gait assessment use wearable within free-living environments to capture habitual information, which is more informative compared to data capture in the lab. Wearables range from inertial to camerabased technologies but pragmatic challenges such as analysis of big data from heterogenous environments exist. For example, wearable camera data often requires manual time-consuming subjective contextualization, such as labelling of terrain type. there is a need for the application of automated approaches such as those suggested by artificial intelligence (AI) based methods. this pilot study investigates multiple segmentation models and proposes use of the PSPNet deep learning network to automate a binary indoor floor segmentation mask for use with wearable camera-based data (i.e., video frames). To inform the development of the AI method, a unique approach of mining heterogenous data from a video sharing platform (YouTube) was adopted to provide independent training data. the dataset contains 1973 image frames and accompanying segmentation masks. When trained on the dataset the proposed model achieved an Instance over Union score of 0.73 over 25 epochs in complex environments. the proposed method will inform future work within the field of habitual free-living gait assessment to provide automated contextual information when used in conjunction with wearable inertial derived gait characteristics. Clinical Relevance-Processes developed here will aid automated video-based free-living gait assessment.

关键词： Deep learning Gait Analysis IMU Terrain Classification Floor Segmentation

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Multi-Modal Deep learning Architecture for Accurate Driver Behavior Recognition in automated Driving Systems

Multi-Modal Deep Learning Architecture for Accurate Driver B...

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international conference on intelligent System and Knowledge engineering, ISKE

作者： Fares Alhaek Weichao Liang Taha M. Rajeh Muhammad Hafeez Javed Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu P.R. China

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