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Target Tracking Algorithm Incorporating Saliency Technology

Procedia Computer Science 2025年 259卷 757-765页

作者： Zhongming Liao Azlan Ismail College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Shah Alam 40450 Selangor Malaysia School of Mathematics and Computer Science Xinyu College Xinyu 338000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia

With the development of video surveillance and mobile robotics, target tracking plays a crucial role in real-time video analysis. In order to improve the performance and robustness of tracking algorithms in complex environments, this study proposes a new target tracking algorithm that enhances target identification and localization by incorporating saliency detection techniques. We first use a deep learning approach to train a saliency detection model that accurately identifies and highlights potential target regions in video frames. In this paper, we combine the traditional correlation filter tracking framework to effectively fuse the saliency map with the original video frames to improve the accuracy and adaptability of tracking. On the basis of this, this paper proposes a new target detection method, on the basis of which, the target tracking accuracy and adaptive ability are further improved by extracting the target image. In the experiments with indoor fixed cameras, the success rates of tracking are 80% and 75% before fusion; after fusion, the success rate reaches 90% and the correct rate reaches 88%. The research results in this paper will help to improve the practicality and effectiveness of target tracking methods and provide new ideas and application examples for mining saliency information in complex scenarios.

关键词： Saliency Technique Target Tracking Algorithm Tracking Success Rate Target Region

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Integrating semantic features in fruit recognition based on perceptual color and semantic template

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Information Processing in Agriculture 2022年第2期9卷 316-334页

作者： Ema Rachmawati Iping Supriana Masayu Leylia Khodra Fauzan Firdaus School of Computing Universitas TelkomBandung 40257Indonesia School of Electrical Engineering and Informatics Institut Teknologi BandungBandung 40132Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing&Big Data Analytics(U-CoE AI-VLB)Institut Teknologi BandungBandung 40132Indonesia

Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts givenby predefined text caption or keywords. Two common fruit recognition models include bagof-features (BoF) and convolutional neural network (ConvNet), which achieve highperformance results. In most cases, the overfitting problem is unavoidable. This problemmakes it difficult to generalize new instances with only a slightly different appearance,although belonging to the same category. This article proposes a new fruit recognitionmodel by associating an object’s low-level features in an image with a high-level *** define a perceptual color for each fruit species to construct a relationship between fruitcolor and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visualfeature is adopted in this model. The experiment was conducted on three different fruitimage datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantictemplate (PCoST), is significantly better than the BoF and ConvNet models in reducing theoverfitting problem.

关键词： Multi-class fruit recognition Perceptual color Semantic template Bag-of-features

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A deep reinforcement learning approach for wind speed forecasting

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Engineering Applications of Computational Fluid Mechanics 2025年第1期19卷

作者： Band, Shahab S. Lin, Ting Jia Qasem, Sultan Noman Ameri, Rasoul Shahmirzadi, Danyal Aslam, Muhammad Shamrooz Pai, Hao-Ting Salwana, Ely Mousavi, Amir International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu Taiwan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Institute of the Information Society Ludovika University of Public Service Budapest Hungary John Von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transf... 详细信息

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence deep reinforcement learning empirical wavelet transform long short-term memory machine learning Renewable energy wind speed forecasting

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Application of Intelligent Systems in Multi-modal Information analytics 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Vijayan Sugumaran Zheng Xu Huiyu Zhou

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Application of Intelligent Systems in Multi-modal Information analytics 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Vijayan Sugumaran Zheng Xu Huiyu Zhou

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Airfoil Inverse Design using Conditional Generative Adversarial Networks

Airfoil Inverse Design using Conditional Generative Adversar...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Xavier Tan Dai Manna Joyjit Chattoraj Liu Yong Xu Xinxing Dao My Ha Yang Feng Computing & Intelligence Department Institute of High Performance Computing A*STAR Singapore School of Informatics & IT Diploma in Big Data Analytics Temasek Polytechnic Singapore Fluid Dynamics Department Institute of High Performance Computing A*STAR Singapore

ISBN: (纸本)9781665476881

Creating an aerodynamic shape, like an airfoil wing, requires many factors to be considered, especially aerodynamic properties such as its lift-to-drag ratio (L/D). Currently, generating feasible airfoil shapes usually requires computationally expensive tools, such as Computational Fluid Dynamics (CFD). In recent years, increasing work has been directed to utilizing machine learning algorithms to synthesize accurate airfoil shapes while reducing the required computational cost. Generative Adversarial Network (GAN) is one of many algorithms to see success in airfoil shape optimization and is shown to generate good airfoils given a small set of training examples. This paper focuses on implementing a conditional GAN (cGAN) based framework with various filters for airfoil inverse design problem. By labelling the training dataset with aerodynamic characteristics separated by pre-defined thresholds to lift-to-drag ratio (L/D) and shape area, the class labels will be able to guide the network to generate different classes of airfoils influenced by these characteristics. Together with layers of Savitzky-Golay (SG) filter and B-Spline Interpolation, the developed model was shown to achieve good performance in generating new airfoils. In addition, we explored the viability of adding Wasserstein loss from Wasserstein GAN into the network architecture, forming a cWGAN-GP. Testing results showed that cWGAN-GP was able to achieve better performance for a specific airfoil class.

关键词： Training Interpolation Machine learning algorithms Shape Atmospheric modeling Computational fluid dynamics Generative adversarial networks

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence big data data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced data: Improving Machine Failure Prediction

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Computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of Computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure *** common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)*** paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas *** original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level *** accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to *** performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and *** showed that the sensitivity of all classifiers increases as the imbalance rate *** with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

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Variational Fuzzy Energy Active Contour Models for Image Segmentation: A Review

Variational Fuzzy Energy Active Contour Models for Image Seg...

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International Conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Tammie Christy Saibin Abdul Kadir bin Jumaat Marina Yusoff Sabah Branch School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Kota Kinabalu Sabah Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia School of Computational Sciences College of Computing Informatics and Mathematics Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor Malaysia

Image segmentation has emerged as one of the main research interests in computer vision over the past decade, which involve many applications, such as medical imaging and biometric identifications. It can be defined as a process of partitioning part of, the whole of, or objects within an image into meaningful regions for further analysis. Image segmentation is divided into variational and non-variational models. Non-variational models are highly dependent on large amounts of data and labels that are not always available and which has led some researchers to focus more on variational models. The most efficient variational model is the active contour model (ACM). However, the main problem faced by variational ACMs is segmenting intensity inhomogeneity images. Hence, this paper presents some strategies that have been utilised in modelling variational ACMs to overcome the problem. One recent strategy is integrating fuzzy theory to help with segmentation. Thus, this paper also highlights a recent type of variational ACM, selective-based variational ACM, that incorporates fuzzy theory in its formulation and discusses the advantages and the challenges of the segmentation model. Finally, several issues and future research directions in variational fuzzy region-based ACM are discussed.

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Dynamic Resource Scheduler for Distributed Deep Learning Training in Kubernetes

Dynamic Resource Scheduler for Distributed Deep Learning Tra...

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Muhammad Fadhriga Bestari Achmad Imam Kistijantoro Anggrahita Bayu Sasmita School of Electrical Engineering and Informatics ITB Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processing & Big Data Analytics (U-CoE AI-VLB) Indonesia

ISBN: (纸本)9781728180380;9781728180397

Distributed deep learning is a method of machine learning that is used today due to its many advantages. One of the many tools used to train distributed deep learning model is Kubeflow, which runs on top of Kubernetes. Kubernetes is a containerized application orchestrator that ease the deployment process of applications. This in turn makes distributed deep learning training done in Kubeflow easier and manageable. Works on a dynamic resource scheduler in Kubernetes for deep learning training have been done before, such as DRAGON that proposed scheduler with autoscaling and gang scheduling capabilities, and OASIS that proposed a utility system with price function. In this work, we propose to combine DRAGON's and OASIS' approach to make a scheduler with weighted autoscaling capabilities and schedule its jobs with gang scheduling. Some modifications are done on DRAGON's autoscaling function. We try to increase the frequency of scaling up function calls and reduce the frequency of scaling down function to make the training process more efficient. Weights are used to determine the priority of each jobs, where jobs with higher resource requirements are considered more important. Weight of each jobs will influence the autoscaling function of the scheduler. Experiment and evaluation done using a set of Tensorflow jobs results in an increase of training speed by over 26% in comparison with the default Kubernetes scheduler.

关键词： Training Deep learning Schedules Estimation Tools Dynamic scheduling informatics

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