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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Yantao Guan, Yunguo Xue, Xiaoping Lu, Rongxing Tongji University College of Electronic and Information Engineering Shanghai201804 China Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States University of New Brunswick Faculty of Computer Science FrederictionNBE3B 5A3 Canada

ISBN: (纸本)9798350351255

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuable insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (BSEQtree) and a predicate encryption scheme for membership testing (PEMT), which enable the edge servers to obliviously match data and queries by traversal over the encrypted BSEQtree. Based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme. © 2024 IEEE.

关键词： Data aggregation

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting 29

GA-Net: Gated Attention Mechanism Based Global Refinement Ne...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Zhao, Shengrong Liang, Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Underwater images are often affected by problems such as light attenuation, color distortion, noise and scattering, resulting in image defects. A novel image inpainting method is proposed to intelligently predict and fill damaged areas for complete and continuous visualization of the image. First, in order to effectively solve the problem of color distortion caused by light refraction in underwater environments, the improved gated attention mechanism is used. This mechanism improves the local details by learning and weighting the important features of the image. Second, gated convolution automatically determines the degree of restoration for each pixel based on local features of the original image. It eliminates distractions such as low contrast and scattering, retaining more original detailed information. By doing so, image inpainting techniques improve the quality and visualization of underwater images. © 2023 IEEE.

关键词： Gated convolution Image inpainting Underwater images

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Analysis of File Carving Approaches: A Literature Review 3rd

Analysis of File Carving Approaches: A Literature Review

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3rd International Conference on Advances in Cyber Security, ACeS 2021

作者： Ramli, Nor Ika Shahirah Hisham, Syifak Izhar Badshah, Gran Faculty of Computing College of Computing and Applied Science Universiti Malaysia Pahang Pahang Pekan26600 Malaysia College of Computer Science King Khalid University Abha Saudi Arabia

ISBN: (纸本)9789811680588

Digital forensics is a crucial process of identifying, conserving, retrieving, evaluating, and documenting digital evidence obtained on computers and other electronic devices. Data restoration and analysis on file systems is one of digital forensic science’s most fundamental practices. There is a lot of research being done in developing file carving approaches and different researches focused on different aspects. With the increasing numbers of literature that are covering this research area, there is a need to review this literature for further reference. A review is carried out reviewing different works of literature covering various aspects of carving approaches from multiple digital data sources including IEEE Xplore, Google Scholar, Web of science, etc. This analysis is done to consider several perspectives which are the current research direction of the file carving approach, the classification for the file carving approaches, and also the challenges are to be highlighted. Based on the analysis, we are able to state the current state of the art of file carving. We classify the carving approach into five classifications which are general carving, carving by specific file type, carving by structure, carving by the file system, and carving by fragmentation. We are also able to highlight several of the challenges for file carving mentioned in the past research. This study will serve as a reference for scientists to evaluate different strategies and obstacles for carving so that they may choose the suitable carving approaches for their study and also future developments. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Digital devices

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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Computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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Robust speech perception and classification-driven deep convolutional neural network with natural language processing

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Alexandria Engineering Journal 2025年 123卷 358-368页

作者： Alshammari, Alya Alzaidi, Muhammad Swaileh A. Alrusaini, Othman Alshuhail, Asma Allafi, Randa Yafoz, Ayman Alsini, Raed Khadidos, Alaa O. Department of Applied Linguistics College of Languages Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of English Language College of Language Sciences King Saud University P. O. Box 145111 Riyadh Saudi Arabia Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Speech recognition systems use computer algorithms to interpret and process spoken words and translate them into text. Speech-to-text, or Speech detection, is the proficiency of a program or machine to find spoken words aloud and translate them into plain text. Speech detection systems are the most common complex systems that use wide-ranging research in computer engineering, linguistics, and computer science. In today's significant data era, several machine learning (ML) approaches for processing raw, unlabelled speech data still need to be improved. Deep learning (DL) models, on the other hand, with their powerful modelling ability for extensive data, are capable of directly processing unlabelled data and have become a crucial area of research in the domain of automated speech recognition. The DL model has recently gained popularity for voice disorders and automated speech detection. DL is an ML that exploits artificial neural networks (ANN) to gain meaningful insights from data. The DL approach demonstrated great success and achieved significant attention in different fields comprising speech processing, computer vision (CV), and natural language processing (NLP). Therefore, this article develops a Deep Learning with Natural Language Processing based Robust Speech Detection (DLNLP-RSD) technique. The DLNLP-RSD technique derives features from the mel-frequency cepstral coefficient (MFCC) technique. Followed by the DLNLP-RSD technique employs a one-dimensional deep convolutional neural network (CNN) approach for the detection and classification of speech. Moreover, the parameter tuning model uses the grasshopper fractals optimization algorithm (GOA) approach. The performance evaluation of the proposed technique highlights its significant performance over other models concerning distinct measures. An extensive simulation analysis is executed to examine the improved results of the DLNLP-RSD model. The experimental validation of the DLNLP-RSD technique portrayed a sup

关键词： Convolutional neural networks

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An Integrated Preprocessing and Drift Detection Approach With Adaptive Windowing For Fraud Detection In Payment Systems (February 2025)

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IEEE Access 2025年 13卷 92036-92056页

作者： Lawati, Hadi M. R. Al Zainal, Anazida Al-Rimy, Bander Ali Saleh Al-Azawi, Mohammad Kassim, Mohamad Nizam Almalki, Sultan Ahmed Alghamdi, Tami Abdulrahman Faculty of Computing Johor Bahru Malaysia Anti-Financial Crime Lab Faculty of Computing Johor Bahru Malaysia University of Portsmouth PAIDS Research Center School of Computing Buckingham Building Lion Terrace PortsmouthPO1 3HE United Kingdom Gulf College Muscat Oman Putrajaya Malaysia Najran University Applied College Computer Department Najran66462 Saudi Arabia Al-Baha University Faculty of Computing and Information Computer Science Department Al-Baha65779 Saudi Arabia

As fraudulent transaction methods evolve rapidly;it becomes progressively more challenging to detect them in payment systems. Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of fraudulent behaviors, resulting in lower detection accuracy and a higher false positive rate. This study proposes a complete framework that brings together advanced data preprocessing, effective drift detection, and a reliable detection model to address these issues. The method uses Mutual Information and SelectKBest for selecting important features, applies ADASYN to handle class imbalance, and adopts Convolutional Neural Networks (CNN) to capture complex transaction patterns. By implementing Early Drift Detection Method (EDDM) and ADaptive WINdowing (ADWIN), the drift can be detected in advance and the system can respond to changes, both gradual and *** framework was evaluated on three datasets, including real-world transactions and mixed-data environments, achieving superior accuracy, precision, and drift detection rates, with values up to 99.99% accuracy and 1.0 respectively. The findings show that the framework can adjust to changing patterns of fraud, reduce false positives, and enhance detection performance. These insights demonstrate the significance of dynamic preprocessing and drift-aware approaches in the context of real-time fraud detection. This also serves as a basis for future work in adaptive fraud detection model research areas such as of integrating online learning for improved speed and efficiency in high-frequency transactional environments. © 2013 IEEE.

关键词： Feature Selection

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Research on Hybrid Markov Prediction Model Based on Clustering

Research on Hybrid Markov Prediction Model Based on Clusteri...

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2022 International Symposium on Computer Applications and Information Systems, ISCAIS 2022

作者： Niu, Wei Cheng, Juan Sun, Dongxu Song, Xiaogang Xi'an Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'an Institute of Applied Optics Xi'an China Faculty of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510655010

In order to reduce the network load and the access delay of web users, caching and prefetching technology are considered to be the main methods to solve the problems of web access delay. At present, the research on caching and prefetching technology often only emphasizes the improvement of replacement strategy or prefetching algorithm, lacks the research on prefetching method, and the improvement of access delay is not ideal. Combined with caching and prefetching methods, a hybrid Markov caching and prefetching model considering the change of user browsing trend over time is established. Aiming at the shortcomings of the current web page prefetching methods, the model uses the hierarchical clustering method to find out the time-varying interval of user browsing behavior. Each time interval uses a Markov chain to predict and maintain the freshness of information. The experimental results show that the proposed method can improve the cache hit rate and reduce the network access delay. © 2022 SPIE

关键词： Websites

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Signet Ring Cell Detection from Histological Images Using Deep Learning

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Computers, Materials & Continua 2022年第9期72卷 5985-5997页

作者： Muhammad Faheem Saleem Syed Muhammad Adnan Shah Tahira Nazir Awais Mehmood Marriam Nawaz Muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool Department of Computer Science UETTaxila47050Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Civil Engineering Imperial College LondonLondon SW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai 50200Thailand

Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous *** and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly *** detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of *** the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature *** extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to ***,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC *** annotations are developed from ground truth(GT)images to perform experimentation on our developed *** introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical *** aim to release the employed database soon to assist the improvement in the SRC recognition research area.

关键词： Mask RCNN deep learning SRC segmentation

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Assessing the Symbiotic Organism Search Variants using Standard Benchmark Functions 3

Assessing the Symbiotic Organism Search Variants using Stand...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Asyikin Zainal, Nurul Din, Fakhrud Zamli, Kamal Z. Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan26600 Malaysia Department of Computer Science and IT University of Malakand KPK Pakistan

Symbiotic Organism Search (SOS) is one of the latest meta-heuristic algorithms created to solve optimization problems. Combining the fact that this new algorithm is parameter-less (no need for tuning) and having a superior performance compared with other meta-heuristic algorithms, the interest to investigate and enhanced this algorithm had emerged. In this paper, we present a new version of SOS by looping the current algorithm rather than doing it one after the other. The target of this paper is to find the effect of changing the structure of algorithm from the original SOS using selected benchmark functions. Our findings indicate that using a loop structure can improve the solution in some of the benchmarks functions as compared to the original SOS. © Published under licence by IOP Publishing Ltd.

关键词： Heuristic algorithms

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