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computing (ICOCO), IEEE International Conference on

作者： Justin Ang Kang Wei Mahayaudin M. Mansor Zuraidah Derasit Mohd Aftar Abu Bakar Ahmad Zia Ul-Saufie Firdaus Mohamad Hamzah Faculty of Business Economics and Accounting HELP University Kuala Lumpur Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Malaysia Centre for Defence Foundation Studies Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

The digital revolution has reshaped the global economy, especially e-commerce. Innovative technologies such as big data analytics, artificial intelligence, and cloud computing drive this transformation, enabling companies to make informed business decisions and influence consumers’ online purchasing behaviour. This study explores empirical evidence for the benefits of using big data analytics (BDA) to influence consumers’ online purchasing decisions in e-commerce. Primary data was collected via an online survey targeting 300 customers with online shopping experience on platforms that have adopted the digital technologies of BDA. Statistical methods used in this study included questionnaire design, reliability test, descriptive analysis, and multiple regression modelling and diagnostics. The findings indicate that the benefits of implementing BDA in e-commerce for information search, recommendation systems, consumer services, dynamic pricing, security and fraud detection are strongly associated with consumers’ online purchasing intentions. The study underscores the importance of businesses leveraging BDA, as the benefits significantly and positively impact consumers’ purchasing intentions in e-commerce while addressing security and fraud detection concerns that undermine its advantage. Nevertheless, we encourage all companies to leverage BDA implementation in their digital marketplace, which ultimately contributes to achieving e-commerce goals, such as increased market reach, cost reduction, and revenue growth.

关键词： Surveys Statistical analysis Companies Big data Reliability engineering Fraud Electronic commerce Security Recommender systems Business

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An Early Detection Model of Prostate Cancer Based on MRI

An Early Detection Model of Prostate Cancer Based on MRI

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IEEE International Conference on data science in Cyberspace (DSC)

作者： Yuankun Liu Lumin Xing Di Wang Xin Nie Xueyan Zhang Di Wu Wenjian Liu Faculty of Data Science City University of Macau China Shandong Provincial Qianfoshan Hospital The First Affiliated Hospital of Shandong First Medical University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Chongqing Jianzhu College Chongqing China School of Information Work Office Shandong Youth University of Political Science Jinan China School of Mathematics Physics and Computing University of Southern Queensland Queensland Australia

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively.

关键词： Computer vision Visualization Accuracy Magnetic resonance imaging Mortality Transformers Feature extraction Reliability Prostate cancer Medical diagnostic imaging

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A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

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Applied Soft computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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Recent Advances in science and Engineering Technology (ICRASET), International Conference on

作者： K. Sashi Rekha V.M. Sivagami R. Usharani T Amutha S. Pushparani Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The cloud services which are now the most common data transmission and endanger organizations’ confidential information, it’s more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats.

关键词： Training Cloud computing Multi-factor authentication Standards organizations Companies Regulation Maintenance Security Protection Monitoring

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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IEEE Conference on Wireless Communications and Networking

作者： Sarah Al–Shareeda Fusun Ozguner Keith Redmill Trung Q. Duong Berk Canberk Electrical and Computer Engineering Department Center for Automotive Research (CAR) The Ohio State University USA BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast UK Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's NL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University UK

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

关键词： 6G mobile communication Performance evaluation Identity-based encryption Authentication Deep reinforcement learning Reproducibility of results Resource management Cryptography Optimization Software development management

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A graph-transformer for whole slide image classification

arXiv

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arXiv 2022年

作者： Zheng, Yi Gindra, Rushin H. Green, Emily J. Burks, Eric J. Betke, Margrit Beane, Jennifer E. Kolachalama, Vijaya B. Department of Computer Science Faculty of Computing & Data Sciences Boston University United States Department of Medicine Boston University School of Medicine United States Department of Pathology & Laboratory Medicine Boston University School of Medicine United States

The material in this template is an edited & LATEX-ified Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected 4, 818 WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy= 91.2 ± 2.5%) based on five-fold cross-validation, and mean accuracy = 82.3 ± 1.0% on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification. © 2022, CC BY.

关键词： Graphic methods

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Grouping District / Cities in Papua Based Poverty Indicators 5

Grouping District / Cities in Papua Based Poverty Indicators

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5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Endartyana, R.Y. Permatasari, E.O. Department of Statistics Faculty of MathematicsComputing and Data Science Sepuluh Nopember Institute of Technology Teknik Mesin road 175 Keputih Sukolilo Surabaya East Java Indonesia

Welfare of the people is a very important thing in a country. One measure of the welfare of society is seen from indicators of poverty. The government conducts a long-term agreement in terms of encouraging change through SDG's. One of the government's target is less poverty in all its forms by 2030. The area in Indonesia which has the highest percentage of poor people in the provinces of Papua. Hence, the groups based Indicators of Poverty in Papua province using C-Means Clustering and Fuzzy C-Means Clustering. The indicators used in this analysis is an indicator of Health, Education Indicators and Household Characteristics indicator. Based on the analysis results obtained Fuzzy C-Means formed 5 group is the best method to classify District / Cities in Papua Province by minimumicdrate value. Areas that need to be the focus of the government to reduce poverty in Papua is the Puncak District. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy systems

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Modeling of infant mortality in west sulawesi using zero inflated poisson regression method 5

Modeling of infant mortality in west sulawesi using zero inf...

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5th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2019

作者： Annisa, R. Permatasari, E.O. Rumiati, A.T. Department of Statistics Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Teknik Mesin road 175 Keputih Sukolilo Surabaya East Java Indonesia

The number of infant deaths, one of which can be reflected in the Infant Mortality Rate (IMR) is one indicator to determine the development of a country's development. The AKB target in the 2015-2019 National Medium-Term Development Plan is 24 deaths per 1,000 live births. Based on the SDKI 2017, the IMR value in West Sulawesi is 42 deaths per 1,000 live births. Factors suspected of affecting infant mortality in West Sulawesi are not only in the health but also in socio-economic fields. data on the number of infant mortality in West Sulawesi is a data count that contains a value of 0. Therefore, the analysis used is Zero Inflated Poisson (ZIP) Regression to determine what factors influence infant mortality in West Sulawesi. The results of the ZIP regression analysis showed that the best model with the smallest AIC and AICc is 324,3407 and 326,7407 were models with a combination of the variables of the percentage of active maternal and child health services, percentage of society with good drinking water adequate access and percentage of society with proper sanitation access. The variable that has a significant effect on the log model is the percentage of active maternal and child health services, percentage of society with good drinking water adequate access and percentage of society with proper sanitation access. In the variable logit model that is influential is the percentage of active maternal and child health services. © Published under licence by IOP Publishing Ltd.

关键词： Potable water

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Learning Uncertainties in Wheel Odometry for Vehicular Localisation in GNSS Deprived Environments 19

Learning Uncertainties in Wheel Odometry for Vehicular Local...

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19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Onyekpe, Uche Palade, Vasile Kanarachos, Stratis Christopoulos, Stavros-Richard G. Coventry University Research Centre for Data Science Institute for Future Transport and Cities Coventry United Kingdom Coventry University Faculty of Engineering and Computing Coventry United Kingdom

ISBN: (纸本)9781728184708

Inertial Navigation Systems (INS) are commonly used to localise vehicles in the absence of Global Navigation Satellite Systems (GNSS) signals. However, they are plagued by noises, which grow exponentially over time during the triple integration computation, leading to a poor navigation solution. We explore the wheel encoder as an alternative to the accelerometer of the INS for positional tracking, and for the first time investigate the capability of deep learning using the Long Short-Term Memory (LSTM) neural network to learn the uncertainty inherent in the wheel speed measurements. These uncertainties could be manifested as changes in the tyre size or pressure, or wheel slips as a result of worn out tyres or wet/muddy road drive. The proposed solution has less integration steps in its computation, therefore providing the potential for a more accurate positioning estimation. Through a performance evaluation on several challenging scenarios for vehicular driving, such as hard braking, quick changes in vehicular acceleration, and wet/muddy road driving, we show that the wheel speed-based positioning approach is able to achieve up to 81.46 % improvement compared to the INS accelerometer approach. © 2020 IEEE.

关键词： Global positioning system

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