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Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

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Multimedia Tools and Applications 2025年第15期84卷 14433-14487页

作者： Ait Mehdi, Mohamed Belattar, Khadidja Souami, Feryel Faculty of Computer Science Data Science and Artificial Intelligence Department Laboratory for Research in Artificial Intelligence University of Science and Technology Houari Boumediene BP 32 El-Alia Algiers Bab-Ezzouar16111 Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers16000 Algeria

Object localization is a critical task in image analysis, often facilitated by artificial intelligence techniques. While the Maximally Stable Extremal Regions (MSER) detection algorithm is a popular choice for local detection, its exhaustive approaches can be algorithmically complex and prone to suboptimal results with improper parameter selection. Various metaheuristic algorithms have been proposed for medical object localization to address this. In this context, four contributions are presented. Firstly, recent metaheuristics, including the Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Heap-based Optimizer (HBO), and Gradient-based Optimizer (GBO), are adapted to tackle the MSER localization problem. These algorithms are rigorously evaluated across diverse medical image datasets using multiple metrics, with their performance statistically validated through the Friedman mean rank test. The second contribution introduces a novel objective function to improve the localization process. The third contribution involves a comparative analysis of the recent algorithms against seven standard metaheuristics specifically designed for MSER localization. Lastly, we present an improved computer-aided diagnosis system that integrates an SVM-based model with Local Binary Patterns (LBP) descriptors extracted from each MSER alongside DenseNet-121 features. Experimental results demonstrate that the HBO and SMA optimizers outperform conventional algorithms, showing a higher diversity, improved exploitation and exploration capabilities, a well-balanced exploration-exploitation trade-off, elevated fitness values, faster convergence speeds, and enhanced computational efficiency. Additionally, we integrate these findings into an improved computer-aided diagnosis system, yielding classification results surpassing state-of-the-art models. These findings highlight the potential advantages of this approach, especially in object detection applications. © The Author(s), under

关键词： Object detection

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An MCDM-Based Methodology for Influential Nodes Detection in a Social Network. Facebook as a Case Study

An MCDM-Based Methodology for Influential Nodes Detection in...

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Qaffou, Issam Ait Rai, Khaoula LISI Laboratory Computer Science Department Faculty of Science Semlalia Cadi Ayyad University Marrakech Morocco Computer System and Vision Laboratory Faculty of Sciences Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783031263835

The identification of influential nodes in a social network is very important for many uses like the control of information spreading. This paper studies how a multi- criteria decision method (MCDM) can find influential persons in a social network. The empirical study performed on a Facebook dataset shows how we can visualize links between profiles and who are the top-k influential ones. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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Bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms 3

Bitcoin price prediction based on financial data, technical ...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Arjmand, Masoud Kazeminia, Saman Sajedi, Hedieh University of Tehran Kish International Campus Department of Computer Engineering Kish Iran NHL Stenden University of Applied Science Department of Computer Vision and Data Science Leeuwarden Netherlands University of Tehran Department of Mathematics Statistics and Computer Science Tehran Iran

ISBN: (纸本)9798350381580

Bitcoin is the leading cryptocurrency with the highest market value among digital currencies. Therefore, predicting the value of Bitcoin can help to understand the entire cryptocurrency market. However, Bitcoin has had a lot of price fluctuations since its inception. In this paper, we are going to forecast the price of Bitcoin using news headline analysis, technical analysis indicators, and historical financial data. The news headlines used in this article are scraped from the Cointelegraph news website, which contains 3988 news headlines related to Bitcoin between 2/7/2020 and 3/8/2021. A transformer pre-trained model on cryptocurrency-related texts called CryptoBERT, which is a BERT-based sentiment analysis model, has been used to analyze the textual data. Also, a novel hybrid 2DCNN-GRU deep learning model has been used to predict the price. To adjust the parameters of this model, a parameter tuning method based on orthogonal arrays called the Taguchi method has been employed. Finally, to examine the proposed model's efficiency, the obtained results have been compared with other deep learning models from the literature review that used text data to predict bitcoin prices. The results show that this model outperformed other models in terms of MAE criterion, while in the other three criteria, namely MSE, RMSE and MAPE, it still demonstrated good results. © 2024 IEEE.

关键词： Forecasting

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Sparse representation scheme with enhanced medium pixel intensity for face recognition

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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Effi-CNN: real-time vision-based system for interpretation of sign language using CNN and transfer learning

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Multimedia Tools and Applications 2025年第6期84卷 3137-3159页

作者： Pranav Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Computer Science & Engineering Department Delhi Technological University New Delhi India

The deaf and mute population has difficulty conveying their thoughts and ideas to others. Sign language is their most expressive mode of communication, but the general public is callow of sign language;therefore, the mute and deaf have difficulty communicating with others. A system that can correctly translate sign language motions to speech and vice versa in real time is required to overcome this communication barrier. Effi-CNN, a vision-based Sign Language Recognition (SLR) system employing transfer learning with EfficientNetB2, is proposed in this paper. We have also developed a system covering sign gestures to text in real time. Our approach was evaluated on eight publicly available datasets, including the Massey University gesture dataset, the ArSL2018 dataset, the MNIST-ASL dataset, and others. When comparing our results to state-of-the-art algorithms, the experimental findings showed that our proposed work is more successful than existing models. Our proposed model delivered an accuracy of 99.90 on MNIST Dataset. The results show that our Effi-CNN surpasses most currently existing solutions and can categorize many gestures with a low error rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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The most tenuous group query

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Frontiers of computer science 2023年第2期17卷 197-208页

作者： Na LI Huaijie ZHU Wenhao LU Ningning CUI Wei LIU Jian YIN Jianliang XU Wang-Chien LEE School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China Laboratory of Big Data Analysis and Processing Guangzhou 510006China School of Artificial Intelligence Sun Yat-Sen UniversityGuangzhou 510006China Department of Computer Science Anhui UniversityHefei 230601China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Department of Computer Science The Pennsylvania State UniversityState College 19019USA

Rtecently a lot of works have been investigating to find the tenuous groups,i.e.,groups with few social interactions and weak relationships among members,for reviewer selection and psycho-educational group ***,the metrics(e.g.,k-triangle,k-line,and k-tenuity)used to measure the tenuity,require a suitable k value to be specified which is difficult for users without background ***,in this paper we formulate the most tenuous group(MTG)query in terms of the group distance and average group distance of a group measuring the tenuity to eliminate the influence of parameter k on the tenuity of the *** address the MTG problem,we first propose an exact algorithm,namely MTGVDIS,which takes priority to selecting those vertices whose vertex distance is large,to generate the result group,and also utilizes effective filtering and pruning *** MTGVDIS is not fast enough,we design an efficient exact algorithm,called MTG-VDGE,which exploits the degree metric to sort the vertexes and proposes a new combination order,namely degree and reverse based branch and bound(DRBB).MTG-VDGE gives priority to those vertices with small *** a large p,we further develop an approximation algorithm,namely MTG-VDLT,which discards candidate attendees with high degree to reduce the number of vertices to be *** experimental results on real datasets manifest that the proposed algorithms outperform existing approaches on both efficiency and group tenuity.

关键词： tenuous group pruning strategy social network group query

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YuNet: A Tiny Millisecond-level Face Detector

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Machine Intelligence Research 2023年第5期20卷 656-665页

作者： Wei Wu Hanyang Peng Shiqi Yu Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Pengcheng Laboratory Shenzhen 518066China

Great progress has been made toward accurate face detection in recent ***,the heavy model and expensive computation costs make it difficult to deploy many detectors on mobile and embedded devices where model size and latency are highly *** this paper,we present a millisecond-level anchor-free face detector,YuNet,which is specifically designed for edge *** are several key contributions in improving the efficiency-accuracy ***,we analyse the influential state-of-theart face detectors in recent years and summarize the rules to reduce the size of ***,a lightweight face detector,YuNet,is *** detector contains a tiny and efficient feature extraction backbone and a simplified pyramid feature fusion *** the best of our knowledge,YuNet has the best trade-off between accuracy and *** has only 75856 parameters and is less than 1/5 of other small-size *** addition,a training strategy is presented for the tiny face detector,and it can effectively train models with the same distribution of the training *** proposed YuNet achieves 81.1%mAP(single-scale)on the WIDER FACE validation hard track with a high inference efficiency(Intel i7-12700K:1.6ms per frame at 320×320).Because of its unique advantages,the repository for YuNet and its predecessors has been popular at GitHub and gained more than 11K stars at https://***/ShiqiYu/***:Face detection,object detection,computer version,lightweight,inference efficiency,anchor-free mechanism.

关键词： Face detection object detection computer version lightweight inference efficiency anchor-free mechanism.

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A Lightweight Network of Groupwise Separable Convolution and vision Transformer for Hyperspectral Image Classification 3

A Lightweight Network of Groupwise Separable Convolution and...

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3rd International Conference on Optics and Machine vision, ICOMV 2024

作者： Zhao, Zhuoyi Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Artificial Intelligence and Computer Vision Laboratory University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China

ISBN: (纸本)9781510680319

Hyperspectral image (HSI) data consists of images with numerous contiguous spectral bands and promotes the extensive applications in the field of remote sensing. Recent approaches based on vision Transformer (ViT) have achieved remarkable performance in HSI classification tasks due to their ability to extract global spatial features and model long-range dependencies. However, ViT has complex network structure, training is challenging and lacks adequate consideration of local spatial and spectral receptive fields in hyperspectral data. To solve the problems above, we propose a lightweight network model known as the Groupwise Separable Convolution and vision Transformer (GSCViT). Firstly, we introduce a parameter-free attention for spectral calibration (SC). Then, we meticulously design a novel convolutional approach named Groupwise Separable Convolution (GSC), which greatly reduce the number of convolutional kernel parameters and effectively capture local spatial-spectral information in HSI data. In addition, we employ Groupwise Separable Multi-Head Self-Attention (GSSA) to replace the traditional Multi-Head Self-Attention (MSA) in ViT, thus can simultaneously attend to local and global spatial features in HSI with lower computational burden. Experiments on two benchmark HSI datasets demonstrate that our GSCViT model achieves excellent accuracy with relatively small training samples and outperforms some existing HSI classification algorithms. © 2024 SPIE.

关键词： Convolution

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Understanding and Detecting Inefficient Image Displaying Issues in Android Apps

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Journal of computer science & Technology 2024年第2期39卷 434-459页

作者：李文杰马骏蒋炎岩许畅马晓星 State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China

Mobile applications(apps for short)often need to display ***,inefficient image displaying(IID)issues are pervasive in mobile apps,and can severely impact app performance and user *** paper first establishes a descriptive framework for the image displaying procedures of IID *** on the descriptive framework,we conduct an empirical study of 216 real-world IID issues collected from 243 popular open-source Android apps to validate the presence and severity of IID issues,and then shed light on these issues’characteristics to support research on effective issue *** the findings of this study,we propose a static IID issue detection tool TAPIR and evaluate it with 243 real-world Android ***,49 and 64 previously-unknown IID issues in two different versions of 16 apps reported by TAPIR are manually confirmed as true positives,respectively,and 16 previously-unknown IID issues reported by TAPIR have been confirmed by developers and 13 have been ***,we further evaluate the performance impact of these detected IID issues and the performance improvement if they are *** results demonstrate that the IID issues detected by TAPIR indeed cause significant performance degradation,which further show the effectiveness and efficiency of TAPIR.

关键词： Android application(app) inefficient image displaying(IID) performance empirical study static analysis

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