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Optimizing Big Data Implementation to Create Business Value and Architecture Proposed in the Banking Industry: A Systematic Review

Optimizing Big Data Implementation to Create Business Value ...

引用

2023 international conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Kristiana, Idha Meyliana, Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The exponential growth of data is compelling organizations to employ data in decision-making. As one of the businesses with an ecosystem that contributes to data growth, banks have challenges in generating insight. A high level of data security is frequently linked to a high level of data access difficulties. This poses a challenge to the implementation of data-driven business, where taking control of our data is one of the best ways to ensure that we not only own data but also have the ability to process and use data to extract business value. Through literature review, a number of challenges to optimizing the implementation of big data analytics in the banking industry were discovered. Data governance refers to the methods and procedures that assist banks in managing and securing data. A big data architecture is presented to address the highlighted issues, particularly with a multi-tiered approach to big data structures. With the adoption of this architecture, it will be simpler to generate business-value-generating insights for the banking industry using big data with accessibility and protection of data. © 2023 IEEE.

关键词： Big data

来源：评论

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A Moving Target Tracking technology for Intelligent Police Based on Kalman Filter Method 4

A Moving Target Tracking Technology for Intelligent Police B...

引用

4th international conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Dang, Longfei Yang, Miao Yi, Kai Si, Binghe Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China Sichuan Police College Department of Computer Science and Technology LuZhou China Sichuan Police College Department of Criminal Technology LuZhou China

ISBN: (纸本)9798350326444

Video image analysis mainly involves the analysis and processing of moving image sequences, which usually involves several processes such as motion detection, object classification, object tracking, and behavior understanding and description. Among them, moving object detection and tracking are at the bottom level of the entire visual monitoring system, and are the most basic methods in video image analysis. They are the foundation for various subsequent advanced processing. This article proposes a target detection and tracking algorithm based on Kalman filtering, which can effectively detect and track moving objects, providing a more efficient technical means for detecting and tracking target suspects in the era of big data. © 2023 IEEE.

关键词： Target tracking

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Combined Diffusion Adaptation on Adaptive Leaky Criterion and Orthogonal Gradient Algorithm 20

Combined Diffusion Adaptation on Adaptive Leaky Criterion an...

引用

20th international conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Sitjongsataporn, Suchada Nurarak, Piyaporn Faculty of Engineering and Technology Mahanakorn University of Technology Dept. of Electronic Engineering Bangkok Thailand Sukhothai Thammathirat Open University School of Science and Technology 9/9 Chaengwattana Rd. Nonthaburi11120 Thailand

ISBN: (纸本)9798350310467

This paper presents a combined diffusion policy on the orthogonal gradient-based algorithm using adaptive averaging leaky criterion. A mixed-node criterion is described by orthogonal projection matrix of its own tap-weight vector, while information of other connected nodes are undisturbed. An adaptive leaky algorithm is applied for fast convergence with low complexity. By minimising a mixed-node cost function, the distributed estimation is verified in terms of adapt-then-combine strategy over the distributed network. Statistical experimental results on system identification examine that a proposed algorithm can provide promising results in form of mean square error criterion. © 2023 IEEE.

关键词： Adaptive algorithms

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Comparative Analysis of Summarization Methods for Skin Care Product Reviews: A Study on BERT, BART, and T5 Models

Comparative Analysis of Summarization Methods for Skin Care ...

引用

2023 international conference on Advanced Mechatronics, Intelligent Manufacture and Industrial automation, ICAMIMIA 2023

作者： Maghfiroh, Nisrina Arintia Abdurrachman Bachtiar, Fitra Muflikhah, Lailil Faculty of Computer Science University of Brawijaya Malang Indonesia University of Brawijaya Intelligent Computing and Visualization Laboratory Faculty of Computer Science Malang Indonesia Science University of Brawijaya Intelligent Computing and Visualization Laboratory Faculty of Computer Malang Indonesia

ISBN: (纸本)9798350309225

The growth of the skin care industry is driving consumers to look for product review information. The skincare product currently widely used by women and men is facial wash products. On the Female Daily website, the most frequently purchased facial wash products are Cetaphil products. With so many purchases of this product, potential buyers want to try the product by reading the reviews first. However, the large number of reviews is a barrier to accessing relevant information. Therefore, effectively summarizing reviews can solve this problem by presenting key information more concisely and digestibly. This study aims to evaluate the effectiveness of three different summary methods, namely BERT, BART, and T5, in summarizing skin care product reviews. The stages in these three models are explained in detail. Evaluation results using the ROUGE Score method with the Cetaphil facial wash product review dataset show that the BART model consistently has a higher average ROUGE Score than the BERT and T5 models. Specifically, the BART model achieved an average ROUGE-1 of 0.87718, ROUGE-2 of 0.80689, and ROUGE-L of 0.87688, while the BERT model achieved an average ROUGE-1 of 0.84591, ROUGE-2 of 0.77585, and ROUGE- L is 0.84479, and model T5 has an average ROUGE-1 of 0.74995, ROUGE-2 of 0.70048, and ROUGE-L of 0.73023. These results suggest that the generative approach implemented by BART is more effective in addressing the diversity and complexity of skin care product reviews. Therefore, using the BART model is a better choice for producing skin care product review summaries that are more informative and useful for consumers. In the future, these findings may contribute to developing more sophisticated product review summary systems. © 2023 IEEE.

关键词： Sales

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Improved ACLPSO algorithm for automatic parameter optimization 5

Improved ACLPSO algorithm for automatic parameter optimizati...

引用

5th international conference on Artificial Intelligence and computer science, AICS 2023

作者： Xu, Huan Liu, Zuhan Wang, Lili Peng, Yun School of Information Engineering Nanchang Institute of Technology Jiangxi Nanchang330099 China School of Science Nanchang Institute of Technology Jiangxi Nanchang330099 China School of Humanities and Arts Nanchang Institute of Technology Jiangxi Nanchang330099 China

ISBN: (纸本)9781510668621

The ACLPSO algorithm, based on dimension specification intervals, adaptively tunes maximum velocity, inertia weight, acceleration coefficient, and learning probability for each dimension. It has demonstrated excellent performance in benchmark function tests involving both single-modal and multi-modal functions. However, to obtain the global optimal or approximate optimal solutions for all executed benchmark functions, it is necessary to manually set appropriate values for the maximum velocity coefficient s and the learning probability coefficient v during the operation of the ACLPSO algorithm. This study introduces an automatic approach to assign values to s and v, relying on function iteration count and test function fitness convergence. This enhancement enables the improved ACLPSO algorithm to directly derive the global optimal or approximate optimal solutions for all benchmark functions, eliminating the need for manual parameter tuning. © 2023 SPIE.

关键词： Benchmarking

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Design of ADCF-based Online Learning Resource Recommendation System for Preschool Education 7

Design of ADCF-based Online Learning Resource Recommendation...

引用

2023 7th international Symposium on computer science and Intelligent Control, ISCSIC 2023

作者： Nan, Jiaopeng Li, Bin XianYang Normal University School of Educational Science XianYang China Hangzhou Hikivision Digital Technology Co. Ltd Hang zhou China

ISBN: (纸本)9798350342987

Online learning has emerged as a brand-new method of knowledge transfer as a result of the Internet and the field of education coming together due to the rapid development of information technology. The proliferation of online learning tools, particularly in the area of preschool education, gives pupils more learning opportunities but also creates issues with selection and learning effectiveness. The goal of this study is to develop an Attention-based Deep Collaborative Filtering (ADCF) preschool online learning resource recommendation system by combining the attention mechanism collaborative filtering algorithm and deep learning algorithm. This system will assist students in selecting the most appropriate online learning resources from a large pool of online learning course resources and increase learning effectiveness. The results show that the average scores of the experimental class's curriculum paper, early childhood teaching activity design and kindergarten game creation were 94.58, 88.15 and 92.43, respectively, which were 6.10%, 7.63% and 1.97% higher compared with the control class's average scores of 89.14, 81.90 and 90.64, respectively. This study has significant practical implications for enhancing the recommendation of online learning resources and raising the standard of preschool education, as can be seen in the conclusion. © 2023 IEEE.

关键词： Recommender systems

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A Secure and Scalable Peer-to-Peer Federated Learning Approach for Handling Veracity in Big Data

A Secure and Scalable Peer-to-Peer Federated Learning Approa...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Iqbal, Maria Tariq, Asadullah Serhani, Mohamed Adel The Superior University Department of Computer Science Lahore Pakistan College of Information Technology United Arab Emirates University Al Ain United Arab Emirates College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350304602

A massive source of terabytes of data is produced consistently from advanced data frameworks and innovation such as distributed/cloud computing, mobile network devices, and the Internet of Things. However, maintaining data veracity in handling such vast and diverse datasets presents significant challenges. In response to these challenges, Federated Learning (FL) emerges as a promising candidate to address the veracity issues in big data analytics. The distributed approach of FL not only ensures data privacy but also mitigates the risk of data breaches and unauthorized access, enhancing data security and veracity. In this paper, we present a Secure and Scalable Peer-to-Peer FL (P2P-FL), a novel approach that tackles the challenges of maintaining data veracity while simultaneously enhancing security and scalability through the utilization of multi-level encryption-decryption processes. This approach eliminates the need for a central server and allows federates to communicate directly, optimizing the efficiency of FL. Differential privacy techniques further ensure individual data privacy. Overall, our approach advances the effectiveness and security of machine learning models in privacy-sensitive environments. In our study, we conducted a comprehensive analysis traditional FL and Peer-to-Peer P2P-FL. We evaluated the performance based on accuracy, loss, and F1 score metrics. The results showcased the effectiveness of our Secure and Scalable Peer-to-Peer FL approach. © 2023 IEEE.

关键词： Wireless networks

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Classification of Rotten Fruits vs Fresh Fruits Using Sequential Model with Convolutional Neural Network 4

Classification of Rotten Fruits vs Fresh Fruits Using Sequen...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chouhan, Manorama Banerjee, P.S. Kumar, Amit Kushwaha, Jatin VIT Bhopal University Department of Computer Science and Engineering Bhopal India Jaypee University of Engineering and Technology Department of Computer Science and Engineering Guna India Jaypee University of Engineering and Technology Department of Computer Science and Engineering Guna India VIT Bhopal University Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350306118

This research employs Convolutional Neural Networks (CNNs) to distinguish between ripe and spoiled fruits using deep learning. The CNN model achieves an impressive accuracy rate of 98.79% in accurately categorizing these two groups. To facilitate model training and evaluation, a comprehensive dataset containing images of both fresh and rotten fruits is utilized. Libraries such as TensorFlow, OpenCV, and matplotlib are employed for efficient dataset processing. The CNN architecture is thoughtfully crafted, incorporating dropout layers to address overfitting and capture intricate fruit image characteristics. A novel approach for categorizing fruit spoilage is introduced, and the study focuses on apples, bananas, and oranges. The model's performance is evaluated using a Kaggle-sourced dataset, yielding an accuracy rate of 98.0% (0.9879), highlighting the effectiveness of the CNN model in discerning between ripe and spoiled fruits. Additionally, the research explores transfer learning techniques for fruit classification. © 2023 IEEE.

关键词： Convolutional neural networks

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Lossless Compression for Quality Scores of Genomic Data Based on High-throughput Sequencing 6

Lossless Compression for Quality Scores of Genomic Data Base...

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6th international conference on computer Information science and Application technology, CISAT 2023

作者： He, Xingxi School of Information Science and Engineering Yunnan University Kunming China

ISBN: (纸本)9781510668546

The FASTQ format is a common genome file format generated by sequencing, mainly composed of identifiers, bases and quality scores. The quality score is an indicator representing the error rate of a base, which corresponds to the base. Although the compression of bases has achieved remarkable developments in recent years, the compression of quality scores remains challenging. This paper mainly improves ACO, a compression algorithm for quality scores. The main content of the algorithm includes the following parts. In terms of traversal quality scores, we comprehensively consider the sequencing process and the principle of FASTQ file generation. Instead of using the traditional raster scanning order, we choose a more reasonable serpentine traversal order. Base changes are introduced as contextual information. The weighted average of the quality score sequence is used as the index of the statistical characteristics of the sequence to perform clustering to improve the coding performance. © 2023 SPIE.

关键词： Serpentine

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A Proposed AI Method for Tracking College Students' Academic Progress 9

A Proposed AI Method for Tracking College Students' Academic...

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9th international conference on Information technology Trends, ITT 2023

作者： Allam, Hesham Khan, Zahoor Letsholo, Keletso Pandya, Bharti Shah, Kavita Ahamed, Jinesh Higher Colleges of Technology Computer and Information Science Dubai United Arab Emirates Higher Colleges of Technology Computer and Information Science Fujairah United Arab Emirates Higher Colleges of Technology Computer and Information Science Abu Dhabi United Arab Emirates Higher Colleges of Technology Business Division Dubai United Arab Emirates

ISBN: (纸本)9798350327502

Educators face significant challenges in monitoring the academic progress of their students in a particular course. Identifying students who are struggling academically can be a daunting task, but teachers can take proactive steps to provide them with additional support once these students come to their attention. Educational institutions today collect vast amounts of data on students from various sources, but they are constantly seeking innovative ways to leverage this information to enhance their reputation and the quality of their instruction. This research delves into the potential of utilizing machine learning algorithms to effectively track students' academic progress and notify teachers about those who may be at risk of failing a class. The translated prediction model presents an easily accessible visual representation that enables teachers to promptly implement appropriate interventions. Our proposed approach employs various machine learning techniques to create a predictive model that can identify students who may be at risk early on, thus mitigating factors that could hinder their learning. This could serve as a foundation for future predictive models to aid university educators in optimizing the overall learning process. © 2023 IEEE.

关键词： Decision trees

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