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IEEE International Conference on Image Processing

作者： Yaozong Gan Guang Li Ren Togo Keisuke Maeda Takahiro Ogawa Miki Haseyama Graduate School of Information Science and Technology Hokkaido University Japan Education and Research Center for Mathematical and Data Science Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Japan

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In this paper, we propose a cross-domain few-shot in-context learning method based on the multimodal large language model (MLLM) for enhancing traffic sign recognition (TSR). We first construct a traffic sign detection network based on Vision Transformer Adapter and an extraction module to extract traffic signs from the original road images. To reduce the dependence on training data and improve the performance stability of cross-country TSR, we introduce a cross-domain few-shot in-context learning method based on the MLLM. To enhance MLLM’s fine-grained recognition ability of traffic signs, the proposed method generates corresponding description texts using template traffic signs. These description texts contain key information about the shape, color, and composition of traffic signs, which can stimulate the ability of MLLM to perceive fine-grained traffic sign categories. By using the description texts, our method reduces the cross-domain differences between template and real traffic signs. Our approach requires only simple and uniform textual indications, without the need for large-scale traffic sign images and labels. We perform comprehensive evaluations on the German traffic sign recognition benchmark dataset, the Belgium traffic sign dataset, and two real-world datasets taken from Japan. The experimental results show that our method significantly enhances the TSR performance.

关键词： Learning systems Image recognition Shape Large language models Roads Training data Assistive technologies

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Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

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Computer Systems science & Engineering 2024年第3期48卷 627-643页

作者： Maha Farouk Sabir Mahmoud Ragab Adil O.Khadidos Khaled H.Alyoubi Alaa O.Khadidos Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia 3Department of MathematicsFaculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz UniversityJeddahSaudi Arabia

Big data and information and communication technologies can be important to the effectiveness of smart *** on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability ***-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make *** to air pollution occurs a main environmental problem in smart city *** effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution ***,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart *** projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city *** achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input *** air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL *** simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

关键词： Sustainability smart cities air pollution prediction ensemble learning coot optimization algorithm

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Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition

arXiv

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arXiv 2024年

作者： Gan, Yaozong Li, Guang Togo, Ren Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Graduate School of Information Science and Technology Hokkaido University Japan Education and Research Center for Mathematical and Data Science Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Japan

Recent multimodal large language models (MLLM) such as GPT-4o and GPT-4v have shown great potential in autonomous driving. In this paper, we propose a cross-domain few-shot in-context learning method based on the MLLM for enhancing traffic sign recognition (TSR). We first construct a traffic sign detection network based on Vision Transformer Adapter and an extraction module to extract traffic signs from the original road images. To reduce the dependence on training data and improve the performance stability of cross-country TSR, we introduce a cross-domain few-shot in-context learning method based on the MLLM. To enhance MLLM’s fine-grained recognition ability of traffic signs, the proposed method generates corresponding description texts using template traffic signs. These description texts contain key information about the shape, color, and composition of traffic signs, which can stimulate the ability of MLLM to perceive fine-grained traffic sign categories. By using the description texts, our method reduces the cross-domain differences between template and real traffic signs. Our approach requires only simple and uniform textual indications, without the need for large-scale traffic sign images and labels. We perform comprehensive evaluations on the German traffic sign recognition benchmark dataset, the Belgium traffic sign dataset, and two real-world datasets taken from Japan. The experimental results show that our method significantly enhances the TSR performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic signs

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Development and analysis of whispering gallery mode model for refractive index sensing 6

Development and analysis of whispering gallery mode model fo...

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6th Photonics Meeting 2023, PM 2023

作者： Batumalay, M. Zain, H.A. Rahim, H.R.A. Harun, S.W. Department of Electrical Engineering University of Malaya Kuala Lumpur50603 Malaysia Faculty of Data Science and Information Technology INTI International University Nilai Negeri Sembilan71800 Malaysia Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

A refractive index sensor based on a whispering gallery mode resonator is numerically investigated in this paper. Whispering gallery mode resonators confine light and can be used in sensing applications, and this work presents the model built using COMSOL software to analyze their sensing performance. This work has investigated the WGM resonators' response to refractive index changes numerically using 2D models in the refractive index ranges 1.000 and 1.333. The resonator showed a sensitivity of 167 nm/RIU in the refractive index range of 1.3331.4 and a sensitivity of 95 nm/RIU in the refractive index range of 1.000-1.010. The sensing resonator has potential applications for biological and chemical analytes. © Published under licence by IOP Publishing Ltd.

关键词： Refractive index

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Efficient Scanning Activity Detection in IoT Networks Using Ensemble Learning 23

Efficient Scanning Activity Detection in IoT Networks Using ...

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2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023

作者： Igried, Bashar Alauthman, Mohammad Alkhaldy, Mohammad Alsarhan, Ayoub Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology Department of Computer Science and Applications The Hashemite University Zarqa Jordan Department of Information Security University of Petra Amman Jordan Department of Business Intelligence and Data Analytics University of Petra Amman Jordan

ISBN: (纸本)9798400708312

The rapidly expanding universe of Internet of Things (IoT) networks has led to greater security risks due to the proliferation of sophisticated threats such as scanning activities, where adversaries use scanning to identify and exploit vulnerabilities in target IoT devices, which often have limited computing power and storage, making them attractive targets;to address this escalating challenge, this research proposes a comprehensive ensemble learning methodology specifically tailored for robust and timely detection of scanning activities in IoT networks, integrating a diverse set of base classifiers including Support Vector Machine (SVM), Neural Network (NN), Decision Trees (DT), and Linear Regression (LR), with predictions from the base models combined using a Linear Regression meta-classifier into a stacked ensemble model;extensive comparative evaluation on an IoT network dataset demonstrates the proposed ensemble technique achieves significantly higher accuracy, detection rate, F-measure, precision, and Area Under ROC Curve (AUC) versus individual traditional machine learning models, and remarkably, exhibits consistent performance across different cross-validation folds, highlighting its inherent stability and validity, suggesting it generalizes well without overfitting;in summary, this research puts forward an efficient and highly effective ensemble learning solution for the timely and precise detection of scanning attacks against IoT networks, serving as a vital step towards strengthening security against escalating threats. © 2023 ACM.

关键词： Network security

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Lanthanide ion modulated dielectric resonance enhancement for nanoscale optical force dye and interferometric scattering microscopy

Lanthanide ion modulated dielectric resonance enhancement fo...

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Optical Manipulation and Its Applications, OMA 2023 - Part of Optica Biophotonics Congress: Optics in the Life sciences 2023

作者： Shan, Xuchen Ding, Lei Wen, Shihui Reece, Peter Wang, Fan School of Physics Beihang University Beijing China School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia School of Mathematical and Physical Sciences Faculty of Science University of Technology SydneyNSW2007 Australia School of Physics The University of New South Wales SydneyNSW Australia

ISBN: (纸本)9781957171210

Nanoscale weak force sensing in aqueous solution is of great interest to researchers from broad areas, including physics and biology. Here we developed a video tracking-based optical tweezers calibration method that enables the detection of the trap stiffness for low refractive index nanoparticles. We found that under resonance conditions, doping lanthanide ions inside a nanocrystal would substantially increase its trap stiffness, achieving a maximum optical trap stiffness of 0.086 pN µm–1 mW–1 for 23.3 nm radius nanoparticles. We further use the dielectric resonance effect to modulate the scattering spectrum of single nanoparticles for multiplexed interferometric scattering microscopy. © 2023 The Author(s).

关键词： Optical tweezers

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A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

arXiv

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arXiv 2024年

作者： Hongo, Wataru Ando, Shuji Tsuchida, Jun Sozu, Takashi Department of Information and Computer Technology Graduate School of Engineering Tokyo University of Science Tokyo Japan Advanced Quantitative Sciences Development Novartis Pharma K.K. Tokyo Japan Department of Information Sciences Faculty of Science and Technology Tokyo University of Science Chiba Japan Department of Data Science Faculty of Data Science Kyoto Women’s University Kyoto Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan

Background: When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using outcome-adaptive lasso was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the outcome-adaptive lasso to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the outcome-adaptive lasso for PS models and various outcome-oriented covariate selection via penalization for outcome models. Methods: We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. In numerical experiments, we assessed bias, standard error, and root-mean-squared error. We applied the AIPW estimators to a clinical trial dataset for reference, comparing their point estimates and standard errors obtained via bootstrap. Results: In the numerical experiments, the performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was

关键词： Mean square error

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Easy data Augmentation in Sentiment Analysis of Cyberbullying

arXiv

引用

arXiv 2023年

作者： Wirawan, Alwan Cahyono, Hasan Dwi Winarno Department of Informatics Faculty of Information Technology and Science Data Universitas Sebelas Maret Surakarta Indonesia

Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at ***/eda_svm. © 2023, CC BY.

关键词： Support vector machines

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Price Prediction of Seasonal Items Using Time Series Analysis

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Computer Systems science & Engineering 2023年第7期46卷 445-460页

作者： Ahmed Salah Mahmoud Bekhit Esraa Eldesouky Ahmed Ali Ahmed Fathalla Faculty of Computers and Informatics Zagazig UniversitySharkeya44523Egypt Information Technology Department the University of Technology and Applied SciencesIbriOman School of Electrical and Data Engineering University of Technology SydneySydney2007Australia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Computer Science Department Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Higher Future Institute for Specialized Technological Studies Cairo3044Egypt Department of Mathematics Faculty of ScienceSuez Canal UniversityIsmailia41522Egypt

The price prediction task is a well-studied problem due to its impact on the business *** are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal *** lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series *** proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based ***,this research tuned a set of well-known predictive models on a real-life *** models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear ***,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are ***,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined *** obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

关键词： Deep learning price prediction seasonal goods time series analysis

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An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

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Computers, Materials & Continua 2022年第10期73卷 493-511页

作者： Muhammad Zubair Rehman Muhammad Aamir Nazri Mohd.Nawi Abdullah Khan Saima Anwar Lashari Siyab Khan Faculty of Computing and Information Technology Sohar UniversitySohar311Sultanate of Oman Soft Computing&Data Mining Centre(SMC) Faculty of Computer Science&Information TechnologyUniversiti Tun Hussein Onn Malaysia(UTHM)Batu Pahat86400Malaysia School of Electronics Computing and MathematicsUniversity of DerbyDerbyDE221GBUnited Kingdom Institute of Computer Sciences and Information Technology The University of Agriculture25120PeshawarPakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Recently, many researchers have used nature inspired metaheuristicalgorithms due to their ability to perform optimally on complex problems. Tosolve problems in a simple way, in the recent era bat algorithm has becomefamous due to its high tendency towards convergence to the global optimummost of the time. But, still the standard bat with random walk has a problemof getting stuck in local minima. In order to solve this problem, this researchproposed bat algorithm with levy flight random walk. Then, the proposedBat with Levy flight algorithm is further hybridized with three differentvariants of ANN. The proposed BatLFBP is applied to the problem ofinsulin DNA sequence classification of healthy homosapien. For classificationperformance, the proposed models such as Bat levy flight Artificial NeuralNetwork (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) arecompared with the other state-of-the-art algorithms like Bat Artificial NeuralNetwork (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distributionback propagation (BatGDBP), in-terms of means squared error (MSE) andaccuracy. From the perspective of simulations results, it is show that theproposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185,and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on *** on WL10 the proposed BatLFANN achieved 99.89899% accuracy withMSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853%accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracywith MSE of 0.006738 which achieve better accuracy as compared to the otherhybrid models.

关键词： DNA sequence classification bat algorithm levy flight back propagation neural network hybrid artificial neural networks(HANN)

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