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13th International Conference on Frontier Computing, FC 2023

作者： Ibrahim, Adam M. Hassan, Ayia A. Li, Jianqiang Pei, Yan Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Science Division University of Aizu Fukushima Aizuwakamatsu965-8580 Japan

ISBN: (纸本)9789819993413

The integration of deep learning (DL) and digital breast tomosynthesis (DBT) presents a unique opportunity to improve the reliability of breast cancer (BC) detection and diagnosis while accommodating novel imaging techniques. This study utilizes the publicly available Mammographic Image Analysis Society (MIAS) database v1.21 to evaluate DL algorithms in identifying and categorizing cancerous tissue. The dataset has undergone preprocessing and has been confirmed to be of exceptional quality. Transfer learning techniques are employed with three pre-trained models - MobileNet, Xception, DenseNet, and MobileNet LSTM - to improve performance on the target task. Stacking ensemble learning techniques will be utilized to combine the predictions of the best-performing models to make the final prediction for the presence of BC. The evaluation will measure the performance of each model using standard evaluation metrics, including accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S). This study highlights the potential of DL in enhancing diagnostic imaging and advancing healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Learning systems

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian 2

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

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2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Mandasari, Atikah Rahayu Nababan, Erna Budhiarti Mawengkang, Herman Informatics Program Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Faculty Of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia

ISBN: (纸本)9798350351613

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages. © 2024 IEEE.

关键词： Semantics

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Literature Review: Analysis of Essential Metadata Attributes of Digital Goods for Transaction in The Virtual World 9

Literature Review: Analysis of Essential Metadata Attributes...

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9th International Conference on information Management and technology, ICIMTech 2024

作者： Martha, Gede Indra Raditya Prabowo, Harjanto Gaol, Ford Lumban Wiputra, Richard BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In online-based virtual worlds such as Metaverse, Online Games, and other online digital spaces, the virtual/digital goods (digital items / digital assets) are fundamental things that must be available to be able to do business & create an economy in the virtual world through monetization & transactions from these digital goods. This research focuses on summarizing and exploring the attributes used in digital goods in the virtual world by extracting attributes in digital goods from other related studies using the Systematic Literature Review methodology & PRISMA Framework comprehensively so that it can be a reference for developers in designing the properties and functions of digital goods in the virtual world that are being developed, which in this case will be in the form of an NFT in the future. Based on research that has been carried out, 3 essential metadata attributes have the most influence on transactions, number 1 Visual Design / Aesthetic (Representative), number 2 Effect (Utilities), and number 3 Statistics / Performance (Utilities) which must be considered in developing/making digital goods in the form of NFTs in the virtual world so that the value of digital goods can attract users to buy or transact them in the virtual world. © 2024 IEEE.

关键词： Virtual environments

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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2023 International Conference on computer science, information technology and Engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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Integrating Data-Driven Security, Model Checking, and Self-adaptation for IoT Systems Using BIP Components: A Conceptual Proposal Model 1st

Integrating Data-Driven Security, Model Checking, and Self-a...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Alsobeh, Anas Shatnawi, Amani Program School of Computing Southern Illinois University CarbondaleIL United States Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan

ISBN: (纸本)9783031337420

Efficiency and convenience have increased as a result of the Internet of Things’ (IoT) expanding adoption across several enterprises. Nevertheless, the dynamic nature of IoT devices severely hinders real-time security breach prevention. Model checking, self-adaptation, and the Behavior-Interaction-Priority (BIP) component model are combined into an application layer to increase data-driven security for IoT systems. An integrated approach is proposed in this paper as a conceptual model for Internet of Things (IoT) security. This approach may have the potential to significantly improve the identification and mitigation of security concerns, including bogus data, malware attacks, and secure system transactions. The framework model is comprised of four primary components: BIP-based Secure Data Collection and Storage (BIP-SDCS), BIP-based Data Analytics (BIP-DA), BIP-based Secure Threat Detection and Response (BIP-STDR), BIP-based Continuous Monitoring (BIP-CM). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Model checking

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Malware Threats Targeting Cryptocurrency: A Comparative Study 2

Malware Threats Targeting Cryptocurrency: A Comparative Stud...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alauthman, Mohammad Al-Qerem, Ahmad Alkasassbeh, Mouhammd Aslam, Nauman Aldweesh, Amjad University Of Petra Department Of Information Security Amman Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa Jordan Princess Summaya University For Technology Computer Science Department Amman Jordan Northumbria University Department Of Computer And Information Sciences Newcastle upon Tyne United Kingdom College Of Computing And It Shaqra University Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

As cryptocurrencies have grown in prevalence and value, associated malware threats have rapidly emerged, exploiting vulnerabilities in wallets, markets, and decentralization mechanisms. This paper provides a comparative review of dominant cryptocurrency malware categories, including ransomware, crypto-jacking, supply chain hijacking, malicious browser extensions, phishing kits, and miner botnets. Analysis of behaviors, targets, propagation tactics, motivations, and detection challenges reveal commonalities and differences across threat types. Examination of recent literature enables comprehensive characterization of the cryptocurrency malware ecosystem to derive insights into evolution trajectories and systemic issues. Ransomware, crypto-jacking, and coordinated botnets represent prevalent threats highlighted, with malware innovation incentivized by pseudonymous payments, irreversible transactions, and criminal usage of mineable privacy-focused coins. Software supply chain attacks and phishing kits showcase propagation risks. Comparative assessment enables identifying research priorities like transaction tracing, behavioral monitoring, and predictive indicators to strengthen adaptive defenses against cryptocurrency-targeting threats. As cryptocurrency permeates society, proactive ecosystem perspectives will grow crucial in responding to malware creativity. © 2024 IEEE.

关键词： Ecosystems

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The Social Media Implications on the Sales of Business Products 3

The Social Media Implications on the Sales of Business Produ...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Jacia, Elvina Dyan, Gabriel Ifan, Amelia Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Alam Sutera Department of Information System Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665464871

The use of technology nowadays does not feel strange. Everyday people who use technology for their daily needs, starting with each other, seeking knowledge, can even earn income by doing business using technology. Social media is a tool that can be used by people to become with each other by creating, sharing, and exchanging information virtually through a network. However, in the current era, the notion of social media is not only about exchanging information, but also to support business products. The problem is how to compete tightly in this modern era in increasing the selling value of business products. The positive impact of social media for sales in business products are huge such as, increase sales, increase product promotion, greater market distribution. On the other side, the negative impact also takes part such as, the more competitors, the products compete fiercely in the market. This research aims to identify more the impact of social media on sales of business products that focused on business, benefit, and factor. The research method that we used is quantitative which focuses on the number of samples of respondents to support the research results and the results are in the form of a graphic that shows the answers of the respondents to the questionnaire. © 2023 IEEE.

关键词： Sales

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Improving the Performance of Multiscene Marketing Video Content Through its Dynamics Adjustments 18

Improving the Performance of Multiscene Marketing Video Cont...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Fornalczyk, Kacper Bortko, Kamil Disterheft, Aneta Jankowski, Jaroslaw West Pomeranian University of Technology Faculty of Computer Science and Information Technology Zonierska 49 Szczecin71-210 Poland

ISBN: (纸本)9788396744784

The use of online video content plays a vital role in marketing strategies and is a significant component of internet usage. The challenge lies in evaluating the impact of video content on user engagement and finding ways to enhance its performance without employing techniques that overwhelm users or prompt ad avoidance behavior. This study investigates the correlation between video dynamics metrics and eye-tracking patterns to determine if user engagement, as indicated by fixations, is influenced by these metrics. The findings demonstrate that dynamic metrics can accurately predict eye-tracking patterns for brief videos and can be applied to measure both inter and intra-scene dynamics in multiscene videos. © 2023 Polish information Processing Society.

关键词： Marketing

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P-ACOHONEYBEE: A Novel Load Balancer for Cloud Computing Using Mathematical Approach

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computers, Materials & Continua 2022年第10期73卷 1943-1959页

作者： Sunday Adeola Ajagbe Mayowa O.Oyediran Anand Nayyar Jinmisayo A.Awokola Jehad F.Al-Amri Computer Engineering Department Ladoke Akintola University of TechnologyLAUTECHOgbomosoNigeria Computer Science Department Ajayi Crowther UniversityOyoNigeria Graduate School Faculty of Information TechnologyDuy Tan UniversityDa NangVietnam Department of Computer Sciences Precious Cornerstone UniversityIbadanNigeria Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Cloud computing is a collection of disparate resources or services,a web of massive infrastructures,which is aimed at achieving maximum utilization with higher availability at a minimized *** of the most attractive applications for cloud computing is the concept of distributed information ***,privacy,energy saving,reliability and load balancing are the major challenges facing cloud computing and most information technology *** balancing is the process of redistributing workload among all nodes in a network;to improve resource utilization and job response time,while avoiding overloading some nodes when other nodes are underloaded or idle is a major ***,this research aims to design a novel load balancing systems in a cloud computing *** research is based on the modification of the existing approaches,namely;particle swarm optimization(PSO),honeybee,and ant colony optimization(ACO)with mathematical expression to form a novel approach called *** experiments were conducted on response time and *** results of the response time of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:2791,2780,2784,2767,2727,and 2599(ms)*** outcome of throughput of honeybee,PSO,SASOS,round-robin,PSO-ACO,and P-ACOHONEYBEE are:7451,7425,7398,7357,7387 and 7482(bps)*** is observed that P-ACOHONEYBEE approach produces the lowest response time,high throughput and overall improved performance for the 10 *** research has helped in managing the imbalance drawback by maximizing throughput,and reducing response time with scalability and reliability.

关键词： ACO cloud computing load balancing swarm intelligence PSO P-ACOHONEYBE honeybee swarm

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