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10th IEEE information technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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Adaptive Varying Contention Window MAC Protocol Based on Underwater Acoustic Propagation Delay

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IEEE Sensors Journal 2023年第13期23卷 15019-15031页

作者： Guo, Yongan Obaidat, Mohammad S. Guo, Jing Wu, Tin-Yu Tey, Fu Jie Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education College of Telecommunications and Information Engineering Nanjing210003 China The King Abdullah II School of Information Technology University of Jordan Amman11942 Jordan The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Amity University UP Noida201301 India Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China National Pingtung University of Science and Technology Department of Management Information Systems Pingtung912301 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan

Underwater acoustic wave propagation time delay and fluctuations are much greater than radio waves. However, many of the protocols used for underwater acoustic networking and communication are designed according to the principles of terrestrial wireless communication networks, failing to give full consideration to utilizing the underwater acoustic propagation and its time delay variation. For this reason, this article proposes an adaptive varying contention window media access control (AVCW-MAC) protocol based on propagation delay. The protocol uses AVCW random backoff mechanism to differently set up the CW of each node based on the different propagation delays from each underwater sensor node to the cluster head node to improve the channel utilization. In addition, to avoid excessive difference in the CW of each node, the backoff factors K1 and K2 are introduced in the AVCW random backoff mechanism to improve the fairness of nodes' accessing the channel. Simulation results show that for the same amount of sensor nodes, the AVCW-MAC protocol has significantly improved network throughput and decreased average end-to-end latency when compared to legacy CW-MAC protocols. © 2001-2012 IEEE.

关键词： Underwater acoustics

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Sentimental Analysis-Based Recommended system for Products Using Machine Learning 8th

Sentimental Analysis-Based Recommended System for Products U...

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8th International Conference on Emerging Research in Computing, information, Communication and Applications, ERCICA 2023

作者： Mamatha Bai, B.G. Likhith, S.R. Itagi, Salma Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Karnataka Bengaluru India Department of Computer Science Engineering Sai Vidya Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819976218

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on *** and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Polarity Sentimental analysis Web scraping

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Transfer learning

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Distributed Consensus Algorithm and Stability for LTI Multi-Agent systems on Directed Graphs 41

Distributed Consensus Algorithm and Stability for LTI Multi-...

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第41届中国控制会议

作者： Yahui Li Simeng Huang Jianyou Huang Mingming Jiang Baoyu Li Jun Zhang China Academy of Launch Vehicle Technology School of System Science and Engineering Sun Yat-Sen University School of Electronic and Information System Jiangsu University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper focuses on the fully-distributed consensus algorithms for a class of multi-agent systems on directed graphs,where every agent can be described by high-order linear time invariant(LTI) ***,a novel consensus algorithm is proposed for the multi-agent ***,taking use of the complex variable theories of linear multivariable feedback systems,the consensus and stability proof of the closed systems is *** the algorithm and proof,the complex roots are permitted in closed-loop characteristic equation of agents,which relaxes some assumptions commonly considered in the available literatures,such as real ***,the constraint condition for them is given,which is one of the key factors to guarantee the stability of the whole ***,designing and proof of the consensus show that the same in dimension among agents is not necessary at all,which provides another convenience for researching multi-agent *** results are provided to illustrate the effectiveness of the proposed approach.

关键词： Multi-agent Consensus,Directed Graph,Linear Time Invariant(LTI) system,Complex Variable Methods,Multivariable Feedback system

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Improved Joint Distribution Adaptation for Fault Diagnosis 8th

Improved Joint Distribution Adaptation for Fault Diagnosis

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8th International Conference on Data Mining and Big Data, DMBD 2023

作者： Gao, Dali Mao, Xiaobin Sheng, Long Wang, Shuangling Science and Technology on Information System Engineering Laboratory Nanjing210023 China

ISBN: (纸本)9789819708369

In the blast furnace (BF) ironmaking process, it is difficult to obtain labeled fault samples and the probability distribution drifts significantly. Therefore, transfer learning has been introduced to fault diagnosis of BF. Most of the existing transfer learning methods achieve domain adaptation by reducing the marginal and conditional distribution discrepancies, without considering the prior distribution. To address this issue, this paper provides a theoretical derivation of the effect of the prior distribution discrepancies on knowledge transfer, and proposes a new method called Improved Joint Distribution Adaptation (IJDA). The model performs reconstruction of the weighted source data to offset the discrepancies of prior distribution, and extracts domain-invariant features by aligning joint distribution to achieve knowledge transfer. In the transfer BF fault diagnosis experiments, the proposed method achieves promising performance improvement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Failure analysis

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Enhancing building pattern recognition through multi-scale data and knowledge graph:a case study of C-shaped patterns

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International Journal of Digital Earth 2023年第1期16卷 3860-3881页

作者： Zhiwei Wei Wenjia Xu Yi Xiao Mi Shu Lu Cheng Yang Wang Chunbo Liu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China The Aerospace Information Research Institute Chinese Academy of SciencesBeijingPeople’s Republic of China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China School of Software Engineering Shenzhen Institute of Information TechnologyShenzhenPeople’s Republic of China Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China School of Geography Science and Geomatics Suzhou University of Science and TechnologySuzhouPeople’s Republic of China

Building pattern recognition is important for understanding urban forms,automating map generalization,and visualizing 3D city ***,current approaches based on object-independent methods have limitations in capturing all visually aware patterns due to the part-based nature of human ***,these approaches also suffer from inefficiencies when applying proximity graph *** address these limitations,we propose a framework that leverages multi-scale data and a knowledge graph,focusing on recognizing C-shaped building *** first employ a specialized knowledge graph to represent the relationships between buildings within and across various ***,we convert the rules for C-shaped pattern recognition and enhancement into query conditions,where the enhancement refers to using patterns recognized at one scale to enhance pattern recognition at other ***,rule-based reasoning is applied within the constructed knowledge graph to recognize and enrich C-shaped building *** verify the effectiveness of our method using multi-scale data with three levels of detail(LODs)collected from AMap,and our method achieves a higher recall rate of 26.4%for LOD1,20.0%for LOD2,and 9.1%for LOD3 compared to existing methods with similar ***,also achieve recognition efficiency improvements of 0.91,1.37,and 9.35 times,respectively.

关键词： Building pattern recognition urban form knowledge graph rule-based reasoning

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The Development and Industry Risks of New Energy Vehicles in China

The Development and Industry Risks of New Energy Vehicles in...

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2022 International Conference on Statistics, Data science, and Computational Intelligence, CSDSCI 2022

作者： Tang, Xi-Ru Wang, Bin Institute of Urban System Engineering Beijing Academy of Science and Technology Beijing100035 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781510661325

The development of the new energy automobile industry is an important direction to accelerate the construction of a powerful automobile country, and it is also an effective means to break the constraints of energy and environment, which is of great strategic significance to China. In this article, based on the perspective of the development of the overall new energy vehicle industry, we analyze the market size and development stage of China's new energy vehicle industry, and explore the characteristics of the industry's development. Further, this paper analyzes the industry risks of the future development of new energy vehicles under the background of the imminent withdrawal of government financial subsidies and the transition of the new energy vehicle industry from policy-driven to market-driven, and provides theoretical support for the healthy and sustainable development of Chinese new energy vehicle industry. © 2023 SPIE.

关键词： Automotive industry

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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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Enhanced Object Detection and Classification via Multi-Method Fusion

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Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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