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A new bi-level deep human action representation structure based on the sequence of sub-actions

Neural Computing and Applications 2025年第2期37卷 985-1008页

作者： Akhlaghian Tab, Fardin Ramezani, Mohsen Afshoon, Hadi Seyedi, Seyed Amjad Moradyani, Atefeh Department of Computer Engineering Faculty of Engineering University of Kurdistan Sanandaj Iran Department of Computer Science University of Kurdistan Sanandaj Iran

Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of sub-actions. A technique is used to segment actions on the time axis based on body movements via an energy diagram. After dividing actions into sub-actions, a novel bi-level deep structure is used to extract their features. Then, the sequence of sub-action features is modeled by a deep network to create the action model. As extracted sub-actions have fewer variations in execution manner, their representation is more stable, and modeling their sequence would be an efficient model. Experimental results on UCF-YouTube, UCF-Sport, and Human Motion DataBase (HMDB) datasets indicate the sustainable performance of this method. Overall, the accuracy of the proposed method is 0.972 on average, while the value for the second-best method is 0.925. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human form models

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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

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Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Ill-condition enhancement for BC speech using RMC method

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International Journal of Speech Technology 2024年第4期27卷 1085-1092页

作者： Ohidujjaman Hasan, Mahmudul Zhang, Shiming Huda, Mohammad Nurul Uddin, Mohammad Shorif Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh Computer Science and Engineering Comilla University Comilla3506 Bangladesh School of Electrical and Information Northeast Agricultural University Harbin150030 China Computer Science and Engineering United International University Dhaka1212 Bangladesh Computer Science and Engineering Green University of Bangladesh Kanchon1460 Bangladesh Computer Science and Engineering Jahangirnagar University Savar1342 Bangladesh

This paper improves the ill-condition of bone-conducted (BC) speech signal by reducing the eigenvalue expansion. BC speech commonly contains a large spectral dynamic range that causes ill-condition for the classical linear prediction (LP) methods. In the field of numerical analysis, we often face the situation where an ill-conditioned case occurs in finding the solution. Principally, eigenvalue expansion causes ill-condition in numerical analysis. To mitigate this problem, the regularized least squares (RLS) technique is commonly used. Motivated by the RLS concept, we derive the regularized modified covariance (RMC) method for BC speech analysis in this study. The RMC method reduces eigenvalue expansion by compressing the spectral dynamic range of the speech signal. Thus, the RMC method resolves the ill-conditioned problem of LP. In experiments, we show that the RMC method provides compressed eigenvalue expansion than the conventional methods for BC speech where synthetic and real BC speeches are considered. The performance of the RMC method is affected by the setting of the regularization parameter. In this paper, the regularization parameter in practice is iteratively and rule-based derived. The RMC method with such a setting provides the best performance for BC speech analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Mapping Technological Trajectories of Edge Computing: A Citation Graph Analysis

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IEEE Internet of Things Journal 2024年第9期11卷 16545-16560页

作者： Wagiu, Elmor Benedict Liu, Chuan-Ming Palopak, Yulianus National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Taiwan University of Science and Technology Department of Information Management Taipei106 Taiwan

The notion of edge computing has recently arisen as a potentially helpful advancement in distributed computing and communication infrastructure. To better understand the evolution and diffusion of technological knowledge in this field, we conducted a citation graph analysis of scholarly publications related to edge computing. Our study aims to identify the critical technological trajectories and research themes that have shaped the development and adoption of edge computing over time. We employ main path analysis (MPA) to construct a citation graph using a comprehensive publications data set from Web of science (WoS) databases. We applied keyword co-occurrence analysis techniques to identify clusters of research topics and their interconnections. Occurrence cluster analysis results indicate that current research in edge computing is primarily centered around the architecture of edge computing, high-speed networks, mobile edge computing, computing systems, and blockchain and security technologies. We also identified the significant papers and contributors in the edge computing field and their impact on the technological trajectory of edge computing. The results reveal 35 significant papers that shape the emergence of four phases of the technology trajectory, including edge computing transition, architecture, edge future, and edge optimization. © 2014 IEEE.

关键词： computer architecture

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IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system

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Multimedia Tools and Applications 2025年第16期84卷 15715-15733页

作者： Ajil, A. Kumar, E. Saravana Department of Computer Science and Engineering The Oxford College of Engineering Visvesvaraya Technological University Karnataka Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources, has emerged as a primary method for addressing other inbuilt problems, such as the underneath or over utilization of physical machines, resource wastage, VM migration, Quality-of-Services (QoS) violations, load balancing, and so on. In this paper, we have introduced a novel workload prediction and load balancing approach which includes two major phases like workload prediction with deep learning and optimal load balancing. In the initial workload prediction stage, we have proposed an Improved Deep Belief Network (IDBN), which efficiently predict the load as under load, overload or equally balanced. Afterwards, the load gets balanced by the utilization of the hybrid optimization named Bald Eagle Assisted Butterfly Optimization Algorithm (BEABOA), which consider the constraints like makespan (70), communication cost (4000), response time (1.1), turnaround time (2), migration cost (0.1) during the process of optimal load balancing. Also, the outcomes demonstrate that this proposed workload prediction and load balancing approach can offer superior outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Resource allocation

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Hybrid CGAN-based plant leaf disease classification using OTSU and surf feature extraction

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Neural Computing and Applications 2024年第23期36卷 14395-14407页

作者： Saraswathi, E. Banu, J. Faritha Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India

Agriculture encompasses a way of life and a profession for the general population. Most global traditions and cultures revolve around agriculture. With the help of advanced farming, agriculture may become more profitable, dependable, and able to use resources and time more effectively. The proposed hybrid CGAN using OTSU and SURF (CGAN-OF) model provides a novel framework for plant leaf disease classification. In proposed CGAN-OF model, contrast-limited adaptive histogram equalization is used for image preprocessing and enhancement. The proposed model utilizes OTSU algorithm to speed up the image segmentation without prior knowledge of the images and SURF algorithm to extract the local features using scale-invariant feature transformation. CGAN increases the input plant village dataset using image generation method and identifies the various plant leaf diseases and classifies them. There are three classifications of leaf diseases: fungi, viruses, and bacteria. Furthermore, these classifications contain approximately 300 diseases. The proposed work identified a minimum of 200 diseases in 18,161 major and minor crop species. The investigational investigations are carried out using the Python Jupyter app with the Kaggle Plant Village Dataset and also leaf samples collected from farmers. The proposed framework achieves 99.2% accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

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Multimedia Tools and Applications 2025年 1-21页

作者： Sudha, R. Maheswari, K.M. Uma Department of Computer Science and Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu India

Lung cancer is a prevalent and deadly disease worldwide, necessitating accurate and timely detection methods for effective treatment. Deep learning-based approaches have emerged as promising solutions for automated medical image analysis. This study proposes an enhanced Mask R-CNN framework tailored specifically for the automatic detection and severity analysis of lung cancer from CT images. The proposed approach consists of three stages namely, pre-processing, lung nodule detection, and segmentation using enhanced Mask R-CNN and severity analysis. Our framework employs a deep convolutional neural network architecture trained on a comprehensive dataset of annotated lung images. By incorporating a region-based convolutional neural network (R-CNN) with a mask prediction branch, our model accurately localizes lung tumors while providing precise pixel-level segmentation masks. To enhance the performance of mask RCNN, the parameter present in the classifier is optimally selected using the adaptive pelican optimization (APO) algorithm. The proposed framework detects lung tumors and provides a comprehensive severity analysis, enabling clinicians to assess cancer stage and progression accurately. Evaluation on a benchmark dataset demonstrates superior detection accuracy and robustness compared to existing methods. Our enhanced Mask R-CNN approach shows promise as a valuable tool for early diagnosis and severity assessment of lung cancer, potentially improving patient outcomes and healthcare efficiency. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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Optimization Design of Output Combining Network Using Irregular Structure for Broadband DPA

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 480-483页

作者： Zhang, Heng Xia, Jing Ni, Zhongpeng Ge, Xiaoshuai Kong, Wa Zhang, Wence Yu, Chao Zhu, Xiao-Wei Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Southeast University School of Information Science and Engineering Nanjing210096 China

This letter presents an optimization design method for broadband Doherty power amplifier (DPA) using an irregular output combining network (OCN). First, the postmatching network and the output matching networks are considered as the overall OCN, and the three-port S-parameters of the OCN are analyzed and calculated using the impedances at power back-off (PBO) and saturation. Subsequently, to improve the feasibility of optimization, several impedances that meet the requirements are used to calculate feasible S-parameter solution sets, which can be used as the optimization target. Finally, an optimization method of the OCN using an irregular structure is proposed. For verification, a 1.2-2.8-GHz broadband DPA is designed and measured. The results show that with the saturated output power exceeding 43.1 dBm, drain efficiencies of 54.1%-74.2% and 50.9%-56.5% are obtained at saturation and 6-dB PBO, respectively. © 2023 IEEE.

关键词： Doherty amplifiers

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Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

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computers, Materials & Continua 2024年第9期80卷 4561-4576页

作者： Atta Rahman Mustafa Youldash Ghaida Alshammari Abrar Sebiany Joury Alzayat Manar Alsayed Mona Alqahtani Noor Aljishi Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision *** detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and ***,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of *** this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB *** approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

关键词： Diabetic retinopathy transfer learning machine learning fundus images binary classification APTOS

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