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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Decision Support Model for Improved Routing in Software Defined Vehicular Ad hoc Networks 18

A Decision Support Model for Improved Routing in Software De...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, Manish Raw, Ram Shringar University School of Information Communication and Technology Guru Gobind Singh Indraprastha University Dept. of Computer Science and Engineering New Delhi India New Delhi India

ISBN: (纸本)9789380544519

the inherent dynamism of Vehicular Ad hoc Networks (VANETs), distinguished by rapid movement and frequent changes in network structure, imposes substantial obstacles in effective route management and optimization. the use of Software Defined Networking (SDN) in VANETs enables a centralized control mechanism that provides flexibility and adaptability in the administration of the network. the SDN Controller in SDN is responsible for route determination. there are many factors like the distance between vehicles, movement direction, connection duration, data transfer rate, speed, and vehicle data transmission range that are involved to determine the route. this study introduces an innovative decision support model for Software Defined Vehicular Ad hoc Networks (SD-VANETs) that use the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to improve the route determination which further enhances the VANETs routing. the model estimates the relative importance of the factors to decide the route by the SDN controller in Sd-VANETs. this work enhances the progress of intelligent routing solutions in VANETs, providing a scalable and efficient way for future ITS advancements. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AHP MCDM in Routing Multi Criteria Decision Making SD-VANET TOPSIS VANET VANET Routing

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TAILORING LANGUAGE GENERATION MODELS UNDER TOTAL VARIATION DISTANCE 11

TAILORING LANGUAGE GENERATION MODELS UNDER TOTAL VARIATION D...

引用

11th international conference on learning Representations, ICLR 2023

作者： Ji, Haozhe Ke, Pei Hu, Zhipeng Zhang, Rongsheng Huang, Minlie Dept. of Comp. Sci. & Tech. State Key Lab of Intelligent Tech. & Sys. China BNRist Center Tsinghua University Beijing100084 China Fuxi AI Lab NetEase Inc. China

the standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method. From a distributional view, MLE in fact minimizes the Kullback-Leibler divergence (KLD) between the distribution of the real data and that of the model. However, this approach forces the model to distribute non-zero (sometimes large) probability mass to all training samples regardless of their quality. Moreover, in the attempt to cover the low-probability regions in the data distribution, the model systematically overestimates the probability of corrupted text sequences, which we conjecture is one of the main reasons for text degeneration during autoregressive decoding. To remedy this problem, we leverage the total variation distance (TVD) with its robustness to outliers, and develop practical bounds to apply it to language generation. then, we introduce the TaiLr objective that balances the tradeoff of estimating TVD. Intuitively, TaiLr downweights real data samples that have low model probabilities with tunable penalization intensity. Experimental results show that our method alleviates the overestimation of degenerated sequences without sacrificing diversity and improves generation quality on a wide range of text generation tasks. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Maximum likelihood estimation

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Performance Analysis of Transfer learning Models for Detecting Autism Spectrum Disorder Using Facial Images 11

Performance Analysis of Transfer Learning Models for Detecti...

引用

11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Sharma, Harendra Sharma, Oshin Srm Institute of Science and Technology Department of Computer Science and Engineering Modinagar Ghaziabad India

ISBN: (纸本)9798350318159

Autism Spectrum Disorder (ASD) is a type of disorder which impacts how individuals communicate interact socially and behave. It is crucial to detect and diagnose ASD on for intervention and support. Over the years advanced deep learning models have shown great potential in various medical fields, including detecting autism. the face of a person can serve as a biomarker because it can reflect the state of the brain making it a convenient and straightforward tool, for diagnosis. In this study we employ learning methods based on convolutional neural networks (CNNs) to identify children with autism by analyzing their facial images. this research paper examines three learning models. Xception, VGG16 and VGG19. After conducting training and validation using the optimized configuration the Xception model showcases the level of performance, with an accuracy rate of 89% on test dataset. On the hand models, like VGG16 and VGG19 achieved accuracies of 76% and 80% respectively. To compare their effectiveness, in detecting autism using data. the study evaluates these models performance based on accuracy, sensitivity, specificity and computational efficiency. © 2023 IEEE.

关键词： Diagnosis

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DIFFERENTIABLE GAUSSIANIZATION LAYERS FOR INVERSE PROBLEMS REGULARIZED BY DEEP GENERATIVE MODELS 11

DIFFERENTIABLE GAUSSIANIZATION LAYERS FOR INVERSE PROBLEMS R...

引用

11th international conference on learning Representations, ICLR 2023

作者： Li, Dongzhuo ExxonMobil Technology & Engineering Company

Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. they exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent tensors of such deep generative models can fall out of the desired high-dimensional standard Gaussian distribution during inversion, particularly in the presence of data noise and inaccurate forward models, leading to low-fidelity solutions. To address this issue, we propose to reparameterize and Gaussianize the latent tensors using novel differentiable data-dependent layers wherein custom operators are defined by solving optimization problems. these proposed layers constrain inverse problems to obtain high-fidelity in-distribution solutions. We validate our technique on three inversion tasks: compressive-sensing MRI, image deblurring, and eikonal tomography (a nonlinear PDE-constrained inverse problem) using two representative deep generative models: StyleGAN2 and Glow. Our approach achieves state-of-the-art performance in terms of accuracy and consistency. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Inverse problems

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PlantLDNet : Disease Detection in Leaf Images Using Deep learning Models 5

PlantLDNet : Disease Detection in Leaf Images Using Deep Lea...

引用

5th international conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Nikhileswar, Gurram Sai Pampana, Surya Sai Jani Anbarasi, L. Bhavana, Chintalapudi Likhitha Nanditha, Rayanki Lakshmi Krishna, Kodela Chaithanya School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385649

automated disease diagnosis in the agricultural field has benefited from the recent advances in big data, deep learning, and computer vision across a wide range of applications. However, intelligent disease prediction is crucial for safely maintaining a good agricultural production. Moreover, unclear disease prognosis is caused by the degree of leaf disease inhomogeneity, the complexity of the background-which includes the low contrast with the surrounding area and the potential for shadows with a similar intensity. this research provides a custom Convolutional Neural Network (CNN) model called PlantLDNet that has hyperparameters configured optimum to distinguish between leaf images showing disease and those without it, hence avoiding the problems associated with vanishing gradient. A comparative study of the proposed custom CNN model PlantLDNet and earlier research works was conducted using the following metrics: precision, recall, accuracy, F1 score, loss accuracy, receiver operating characteristic (ROC) curves, and Area Under Curve (AUC) measure. the comparison findings show that the proposed PlantLDNet model has the highest classification prediction effectiveness (99.85%) with less computational complexity. It also has an elevated high F1-score of 93% when trained for Early Bright, 98% for Healthy, and 92% for Late Bright. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluation of Healthprompt for Zero-shot Clinical Text Classification 11

Evaluation of Healthprompt for Zero-shot Clinical Text Class...

引用

11th IEEE international conference on Healthcare Informatics, ICHI 2023

作者： Sivarajkumar, Sonish Wang, Yanshan University of Pittsburgh Intelligent Systems Program School of Computing and Information Pittsburgh United States University of Pittsburgh School of Health and Rehabilitation Sciences Department of Health Information Management Pittsburgh United States

ISBN: (纸本)9798350302639

this paper presents an evaluation of the Healthprompt, a prompt-based zero-shot clinical text classification framework. the lack of publicly available datasets and the expensive data annotation in the clinical domain make traditional NLP models difficult to train. To overcome this issue, Healthprompt utilizes Pre-trained Language Models (PLMs) and prompt-based Zero-Shot learning (ZSL) to perform clinical NLP tasks without additional training data. However, the original Healthprompt paper missed the error analysis and ablation study of the framework. this paper conducts an error analysis and an ablation study on Electronic Health Records(EHR) notes to understand the capabilities and limitations of the Healthprompt framework for clinical text classification. the results provide insight into the potential and limitations of prompt-based zeroshot learning for clinical NLP tasks and offer suggestions for improvements to the Healthprompt framework, and for the future development of prompt-based ZSL. © 2023 IEEE.

关键词： learning systems

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Ensemble learning Prediction of Bus Travel Time Based on Big AVL data 13th

Ensemble Learning Prediction of Bus Travel Time Based on Big...

引用

13th international conference on Green intelligent Transportation Systems and Safety

作者： Liu, Yanjun Zhang, Hui Shandong Jianzhu Univ Sch Transportat Engn Jinan 250101 Peoples R China

ISBN: (纸本)9789819730070;9789819730056;9789819730049

Unreliable bus transit affects both providers and users in many ways. With the widespread use of the automated vehicle location (AVL) system, rational utilization of AVL data to analyze bus service and predict bus travel time will play a critical role in regulating the entire public transport system and attracting more passengers. In this paper, we conducted a systematic analysis of the reliability of the bus travel time. the bus travel time shows a bimodal distribution and fits a lognormal distribution. Moreover, the variation in bus travel time is larger on weekdays than on weekends. then, ensemble-based prediction models were used to predict travel time based on relevant findings. this study has promising implications for understanding bus travel time and offers a solution to rationalize bus scheduling for bus authorities.

关键词： Public transport service AVL data Travel time prediction Ensemble learning

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Achieving Energy Efficiency in Smart Home by Using Machine learning Algorithm for Prediction 11

Achieving Energy Efficiency in Smart Home by Using Machine L...

引用

11th international Scientific conference on Computer Science, COMSCI 2023

作者： Kutseva, Milka Technical University of Sofia Plovdiv Branch Department of Computer Systems and Technologies 25 Tzanko Dustabanov Plovdiv4000 Bulgaria

ISBN: (纸本)9798350325256

this article presents a system that can be utilized in Smart Homes without reducing the level of comfort for the residents. We provide a frequent sequential pattern mining approach that is appropriate for providing real-world event data for Smart Homes. With the aid of a network connection, the Smart Home allows the remote monitoring and control of equipment. In this paper we present the seven-layered IoT architecture, with the help of which the Smart Home was implemented. the main goal is to build a system that analyzes the monthly consumption data and predicts the future monthly consumption based on it. this was achieved by using machine learning approach in particular ***. there are different types of machine learning algorithms that are available for this purpose. this paper focuses mainly on forecasting algorithm called Singular Spectrum Analysis (SSA). © 2023 IEEE.

关键词： Forecasting

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Analysis of Sensor Signals with Machine and Deep learning Algorithms 11

Analysis of Sensor Signals with Machine and Deep Learning Al...

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11th international Scientific conference on Computer Science, COMSCI 2023

作者： Yotov, Ognyan Borislavov Aleksieva-Petrova, Adelina Plamenova Technical University of Sofia German Engineering and Industrial Management Faculty 8 Kliment Ohridski blvd Sofia1000 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9798350325256

Industry 4.0 relies heavily on data generation and analysis. Sensor signals are difficult for analysis using traditional methods and mathematical techniques. Machine and Deep learning algorithms in combination with mathematical transformations offer effective new ways of approaching the difficult problems of processing sensor signals. the paper's goal is to propose and validate the data-driven prediction model to analyze sensor data based on machine and deep learning algorithms. the models show promising results in predicting wear values and open the problem to further research. © 2023 IEEE.

关键词： learning algorithms

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TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRESENTATION 11

TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRE...

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11th international conference on learning Representations, ICLR 2023

作者： Wu, Hongqiu Liu, Yongxiang Shi, Hanwen Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. this paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pretraining where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 → 61.1), HellaSWAG (93.0 → 94.9), ANLI (68.1 → 69.3). © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Global optimization

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