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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Cardinality estimation for property graph queries with gated learning approach on the graph database

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Multimedia Tools and Applications 2025年第11期84卷 9159-9183页

作者： He, Zhenzhen Yu, Jiong Du, Xusheng Guo, Binglei Li, Ziyang Li, Zhe School of Information Science and Engineering Xinjiang University Urumqi China School of Computer Engineering Hubei University of Arts and Science Xiangyang China School of Software Xinjiang University Urumqi China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong

With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucial for property graph data model, which usually involves entities, multiple joins, and various types of properties (key-value pairs). While learning-based approaches have been used in query optimization, estimating the cardinality of property graph queries is still particularly challenging. In this paper, we first formally represent each property graph query and then divide the estimation process into three phases: query execution, training data generation, and model training. Secondly, we construct a data pool to deal the updating query workloads in the training data generation phase. Thirdly, we utilize the gate mechanism to develop a cardinality estimation framework based on deep learning neural networks for property graph queries in the Neo4j database, and we propose a hybrid loss function to optimize the training process. Finally, we adopt a method of pre-aggregating the underlying data to speed up query execution in the first phase. The result of the experiment on two real-world datasets shows that our optimized methods can effectively improve the estimation accuracy of the deep learning model, and our query estimator outperforms other deep learning models compared in terms of estimation accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Query processing

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Intelligent Resource Allocations for software-Defined Mission-Critical IoT Services

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computers, Materials & Continua 2022年第11期73卷 4087-4102页

作者： Chaebeen Nam Sa Math Prohim Tam Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Heterogeneous Internet of Things(IoT)applications generate a diversity of novelty applications and services in next-generation networks(NGN),which is essential to guarantee end-to-end(E2E)communication resources for both control plane(CP)and data plane(DP).Likewise,the heterogeneous 5th generation(5G)communication applications,including Mobile Broadband Communications(MBBC),massive Machine-Type Commutation(mMTC),and ultra-reliable low latency communications(URLLC),obligate to perform intelligent Quality-of-Service(QoS)Class Identifier(QCI),while the CP entities will be suffered from the complicated massive HIOT ***,the existing management and orchestration(MANO)models are inappropriate for resource utilization and allocation in large-scale and complicated network *** cope with the issues mentioned above,this paper presents an adopted software-defined mobile edge computing(SDMEC)with a lightweight machine learning(ML)algorithm,namely support vector machine(SVM),to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation ***,the SVM algorithm plays an essential role in performing QCI ***,the software-defined networking(SDN)controller allocates and configures priority resources according to the SVM classification ***,the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint *** on the E2E experimentation metrics,the proposed scheme shows remarkable outperformance in key performance indicator(KPI)QoS,including communication reliability,latency,and communication throughput over the various powerful reference methods.

关键词： Mobile edge computing Internet of Things software defined networks traffic classification machine learning resource allocation

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AI-Enabled Grouping Bridgehead to Secure Penetration Topics of Metaverse

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computers, Materials & Continua 2022年第12期73卷 5609-5624页

作者： Woo Hyun Park Isma Farah Siddiqui Nawab Muhammad Faseeh Qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan

With the advent of the big data era,security issues in the context of artificial intelligence(AI)and data analysis are attracting research *** the metaverse,which will become a virtual asset in the future,users’communication,movement with characters,text elements,etc.,are required to integrate the real and ***,they can be exposed to ***,various hacker threats *** example,users’assets are exposed through notices and mail alerts regularly sent to users by *** the future,hacker threats will increase mainly due to naturally anonymous ***,it is necessary to use the natural language processing technology of artificial intelligence,especially term frequency-inverse document frequency,word2vec,gated recurrent unit,recurrent neural network,and long-short term ***,several application versions are ***,research on tasks and performance for algorithm application is *** propose a grouping algorithm that focuses on securing various bridgehead strategies to secure topics for security and safety within the *** algorithm comprises three modules:extracting topics from attacks,managing dimensions,and performing ***,we create 24 topic-based *** normal and spam mail attacks to verify our algorithm,the accuracy of the previous application version was increased by∼0.4%-1.5%.

关键词： Metaverse security computational linguistics grouping bridgehead AI

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Classification and Segmentation of Gastroenterology Images Using Saliency Mapping in Convolutional Neural Network 21

Classification and Segmentation of Gastroenterology Images U...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Khan, Yusra Shaukat, Arslan Riaz, Farhan Department of Computer and Software Engineering Islamabad Pakistan School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9798331516680

This research work has been done towards the treatment of Gastrointestinal (GI) Cancer by experimenting as to how a computer aided design can help oncologists classify and segment GI Cancer using Gastroenterology (GE) Images. The concept of Attention Module by the name of Convolution Block Attention Module (CBAM) using Convolutional Neural Network (CNN) layers has been utilized to achieve saliency mapping in these images. The different layers of the CNN are tweaked and the hyper parameters are consistently checked and changed to achieve maximum accuracy. The CBAM Algorithm is divided into Channel Attention (CA) and Spatial Attention (SA) module. The Channel Attention has been implemented in the HSV Color Space for finding the best color space for segmentation purposes of the Chromo endoscopy medical images. Classification, followed by segmentation, is again implemented using CNN based model for normal and abnormal cancer images. For classification, 92% test accuracy has been achieved, while 70% Dice Similarity Coefficient has been obtained for segmentation. The results achieved are competitive as compared to the previous reported results on the GE images dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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TrafficNet: A Hybrid CNN-FNN Model for Analysis of Traffic Accidents in Seoul

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Journal of Computing Science and engineering 2023年第4期17卷 182-194页

作者： Mustafa, Ghulam Cho, Seong-je Hwang, Youngsup Department of Computer Engineering Sun Moon University Asan Korea Republic of Department of Software Science Dankook University Yongin Korea Republic of

The escalating global trend of traffic accidents with subsequent loss of lives is a matter of grave concern that requires immediate attention. Extensive efforts have been made to mitigate accidents and develop effective prevention strategies. This research paper focuses on a comprehensive analysis of traffic accidents in Seoul, aiming to identify factors and accident types that contribute to increased severity. To achieve this, we introduced a new approach called "TrafficNet: A Hybrid CNN-FNN Model" to evaluate effects of various parameters on the severity of traffic accidents in Seoul. Our main objective was to classify accidents into four distinct levels of severity: minor injuries, slander, fatalities, and injury reports. To assess the effectiveness of our proposed model, we conducted comprehensive experiments using publicly available traffic accident data provided by Seoul Metropolitan Government. These experiments involved six different models, including five machine learning models (decision tree, random forest, k-nearest neighbor, gradient boosting, and support vector machine) and one deep learning model (multilayer perceptron). The proposed model demonstrated exceptional performance, surpassing all other models and previous research findings using the same dataset. On the test dataset, TrafficNet achieved an impressive accuracy of 93.98% with a precision of 94.31%, a recall of 93.98%, and an F1-score of 93.89%. Copyright 2023. The Korean Institute of Information Scientists and Engineers

关键词： Support vector machines

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Evaluation of Retired Lithium-ion Batteries for Second-life Applications through Electrochemical Impedance Spectroscopy 14

Evaluation of Retired Lithium-ion Batteries for Second-life ...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Anekal, Latha Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9798331516116

The rapid growth of electric transportation will generate a substantial number of retired batteries. Recycling the retired LIBs is being looked at worldwide to recover the expensive raw materials from used batteries. Alternatively, the retired electric vehicle (EV) batteries can be reused directly or refurbished for second-life applications. Nevertheless, Lithium-ion batteries (LIBs) degrade during their first-life usage, hence ensuring safety, efficiency, and reliability in second-life applications is crucial. This study investigates LIB degradation using electrochemical impedance spectroscopy (EIS) to assess the battery condition at the end of its primary use. A Samsung 21700 nickel cobalt aluminium (NCA) battery is cycled to 80% of its initial capacity and equivalent circuit model (ECM) parameters obtained through electrochemical impedance spectroscopy (EIS) are analyzed. Individual ECM parameters are evaluated to acquire the health indicators. Although the battery capacity decreased by 20%, resistive components such as bulk resistance, charge transfer resistance and Warburg resistance increased by 140%, 150% and 132% respectively. Given the relative capacity and resistance, the battery is well-suited for second-life applications such as grid frequency regulation, uninterruptible power supply, electric forklifts, and material handling equipment. Other health indicators such as Loss of lithium inventory (LLI) and loss of active materials (LAM) are also captured. © 2025 IEEE.

关键词： Lithium-ion batteries

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A model to measure the spread power of rumors

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13787-13811页

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad-Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cell proliferation

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NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

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computers, Materials & Continua 2022年第11期73卷 2985-3001页

作者： Woo Hyun Park Isma Farah Siddiqui Dong Ryeol Shin Nawab Muhammad Faseeh Qureshi Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwon16419Korea Department of Software Engineering Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Education Sungkyunkwan UniversitySeoul03063Korea

have been focused on addressing the Covid-19 pandemic;for example,governments have implemented countermeasures,such as quarantining,pushing vaccine shots to minimize local spread,investigating and analyzing the virus’characteristics,and conducting epidemiological investigations through patient management and ***,researchers worldwide require funding to achieve these ***,there is a need for documentation to investigate and trace disease ***,it is time consuming and resource intensive to work with documents comprising many types of unstructured ***,in this study,natural language processing technology is used to automatically classify these *** used statistical methods include data cleansing,query modification,sentiment analysis,and ***,owing to limitations with respect to the data,it is necessary to understand how to perform data analysis suitable for medical *** solve this problem,this study proposes a robust in-depth mixed with subject and emotion model comprising three *** first is a subject and non-linear emotional module,which extracts topics from the data and supplements them with emotional *** second is a subject with singular value decomposition in the emotion model,which is a dimensional decomposition module that uses subject analysis and an emotion *** third involves embedding with singular value decomposition using an emotion module,which is a dimensional decomposition method that uses emotion *** accuracy and other model measurements,such as the F1,area under the curve,and recall are evaluated based on an article on Middle East respiratory syndrome.A high F1 score of approximately 91%is *** proposed joint analysis method is expected to provide a better synergistic effect in the dataset.

关键词： Computational linguistic AI epidemic healthcare classification

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Framework for Agent-Based Multistage Application Partitioning Algorithm in Mobile Cloud Computing

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SN computer Science 2024年第4期5卷 330页

作者： Kanwal, Asia Amjad, Tehmina Ashraf, Humaira Department of Computer Science and Software Engineering International Islamic University H10 Islamabad 44000 Pakistan

The idea of computational offloading is quickly catching on in the world of mobile cloud computing (MCC). Today’s applications have heavy demands on power and computing resources, creating issues with energy consumption, storage capacity, and mobile device performance. Mobile devices may efficiently offload their calculations to cloud servers using the offloading paradigm and then get the processed results back onto the device. The investigation relates to identifying the specific application components that should be offloaded and run remotely and which parts are supposed to be treated locally. The applications need to be partitioned to differentiate between remote and local codes. In this paper, an agent-based multistage graph partitioning (ABMP) scheme is proposed. The framework of the scheme is based on three-tier architecture that includes mobile, cloudlet, and cloud for the execution of application tasks. The main goal is to provide an efficient partitioning and offloading scheme in the mobile cloud computing area. The results show that incorporating both agent-based multistage graph partitioning and offloading algorithms yields superior performance as compared to previous methods in terms of reducing execution costs and conserving battery life for mobile devices. © The Author(s) 2024.

关键词： Agent-based partitioning Application partitioning Edge computing Mobile cloud computing Multistage graph partitioning Ready time partitioning offloading

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