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

作者： Prawira, Indra Andryani, Nur Afny Catur Yufriadi, Ebnu Bina Nusantara University Faculty of Digital Communication and Hotel & Tourism Mass Communication Program Communication Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Digital tourism village potentially accelerates rural development hence improving a country's economy. The Indonesian government has developed hundreds of tourism villages in recent years but has yet to popularize the development of digital villages. technology design and development are pivotal factors in tourism villages' digitalization. Therefore, the development digital tourism village must consider village readiness by measuring technology adoption readiness or technology acceptance of the corresponding stakeholders. This paper presents the technology acceptance analysis in the context of a digital tourism village. We use a quantitative analysis approach using the TAM-UTAUT model with data collected from a questionnaire of 101 villagers (n=101) in Pasanggrahan Village, West Java, Indonesia. This research uses machine learning with clustering procedures to analyze the level of technology adoption readiness by implementing K-means Clustering algorithm. In addition, to observe the significance of each influencing factor to the technology adoption readiness level, feature importance analysis approach using Extra tree classifier algorithm is employed. The research finds that attitude toward use, perceived ease of use, and facilitating condition-have a considerable impact on the degree of readiness for technology adoption. On the other hand, the perceived utility and social impact are found to have minimal bearing on the preparedness level for technology adoption. Perceived ease of use and facilitating conditions are the most important factors to afford the acceleration of technology adoption readiness. The research contributes to the development of a digital tourism village and the analysis of technology adoption readiness using machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China Communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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MSF-Net: Multi-Scale Feedback Reconstruction for Guided Depth Map Super-Resolution

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IEEE Transactions on Circuits and Systems for Video technology 2024年第2期34卷 709-723页

作者： Wang, Jin Li, Chenyang Shi, Yunhui Wang, Dan Wu, Mu-En Ling, Nam Yin, Baocai Beijing University of Technology Faculty of Information Technology Beijing100124 China National Taipei University of Technology Department of Information and Finance Management Taipei10608 Taiwan Santa Clara University Department of Computer Science and Engineering Santa ClaraCA95053 United States

Guided depth map super-resolution (GDSR) is one of the mainstream methods in depth map super-resolution, as high-resolution color images can guide the reconstruction of the depth maps and are often easy to obtain. However, how to make full use of extracted guidance information of the color image to improve the depth map reconstruction remains a challenging problem. In this paper, we first design a multi-scale feedback module (MF) that extracts multi-scale features and alleviates the information loss in network propagation. We further propose a novel multi-scale feedback network (MSF-Net) for guided depth map super-resolution, which can better extract and refine the features by sequentially joining MF blocks. Specifically, our MF block uses parallel sampling layers and feedback links between multiple time steps to better learn information at different scales. Moreover, an inter-scale attention module (IA) is proposed to adaptively select and fuse important features at different scales. Meanwhile, depth features and corresponding color features are interacted using cross-domain attention conciliation module (CAC) after each MF block. We evaluate the performance of our proposed method on both synthetic and real captured datasets. Extensive experimental results validate that the proposed method achieves state-of-the-art performance in both objective and subjective quality. © 1991-2012 IEEE.

关键词： Color

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The Application of Artificial Intelligence in Alzheimer’s Research

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Tsinghua science and technology 2024年第1期29卷 13-33页

作者： Qing Zhao Hanrui Xu Jianqiang Li Faheem Akhtar Rajput Liyan Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Medicine Tsinghua UniversityBeijing 100084China Department of Computer Science Sukkur IBA UniversitySukkur 65200Pakistan Department of Neurology Tsinghua University Yuquan HospitalBeijing 100040China

Alzheimer's disease(AD)is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function,but the etiology of AD is still *** the explosive growth of electronic health data,the application of artificial inteligence(Al)in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches,and improving the disease's diagnostic and prognostic *** paper first briefly introduces Al technologies and applications in medicine,and then presents a comprehensive review of Al in *** simple,it includes etiology discovery based on genetic data,computer-aided diagnosis(CAD),computer-aided prognosis(CAP)of AD using multi-modality data(genetic,neuroimaging and linguistic data),and pharmacological or non-pharmacological approaches for treating ***,some popular publicly available AD datasets are introduced,which are important for advancing Al technologies in AD ***,core research challenges and future research directions are discussed.

关键词： Alzheimer's disease artificial intelligence etiology discovery computer-aided diagnosis computer-aided prognosis treatment

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Development of Charge Integrator Circuits with a Microcontroller for Cylindrical Capacitive Sensors in Paddy Moisture Balance Detectors 13

Development of Charge Integrator Circuits with a Microcontro...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Tokampang, Sudarat Apirattanon, Nattapong Sisuk, Noppadon Kaewpoonsuk, Anucha Naresuan University Faculty of Science Deparment of Physics Phitsanulok Thailand Phibunsongkhram Rajabhat University Program of Computer Engineering Faculty of Industrial Technology Phitsanulok Thailand

ISBN: (纸本)9798331543952

This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on the principle of the dielectric constant of paddy, which changes according to the moisture content within them. The system consists of a moisture detection section utilizing a cylindrical capacitor, a signal generator operating at a frequency of 500 Hz with a microcontroller, a signal conditioning circuit converting the signal to voltage, and a processing unit. Testing was conducted with paddy at five different moisture levels, demonstrating the ability to accurately determine the moisture content of the rice. When compared to the commercially available PM-450TH moisture meter, the results showed a linear response across the measurement range, with an R2 value of 0.9824 and an average error of 2.6%. This technology has the potential for further development and application to other types of seeds in the future. © 2025 IEEE.

关键词： charge integrator circuits cylindrical capacitive sensors paddy moisture

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Fake Reviews Detection Using Deep Learning: A Survey. 2

Fake Reviews Detection Using Deep Learning: A Survey.

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Abdelmohsen, Yasmeen Wassif, Khaled Ramadan, Nagy Cairo University Faculty of Graduate Studies for Statistical Research Department of Information Systems and Technology Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Department of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

Online shopping and e-commerce platforms are increasingly plagued by fake reviews, which undermine consumer trust and distort purchasing decisions. This survey addresses the critical need for effective detection mechanisms by exploring the application of deep learning techniques in identifying fake reviews. It carefully examines both supervised and unsupervised deep learning models, presenting their use cases, the features they prioritize, and their effectiveness in different scenarios. Key contributions of this survey include a comprehensive review of current methodologies, a critical comparison of their practical applications, and a discussion on the challenges of implementing these models. Furthermore, the survey identifies promising future research directions aimed at enhancing the accuracy and reliability of fake review detection systems. By synthesizing insights across various studies, this paper contributes significantly to the academic and practical understanding of deep learning's potential to combat the issue of fake reviews in digital commerce. © 2024 IEEE.

关键词： Contrastive Learning

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Stock Monitoring and Localization Systems for FIFO Method Using RFID Based Pressure Sensing Tag and AI

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2024年第9期144卷 894-900页

作者： Hadi, Danang Kumara Song, Zequn Rahmadya, Budi Sun, Ran Takeda, Shigeki Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan Department of Agroindustrial Technology Universitas Muhammadiyah Jember Jember68124 Indonesia Computer System Department Faculty of Information Technology Andalas University Padang25175 Indonesia

This research introduces real-time monitoring and localizing product stock using the First-In-First-Out (FIFO) method with radio frequency identification (RFID) pressure sensing tags. The proposed FIFO system has RFID pressure sensing tags that detect pressure changes, generate sensor codes, and estimate a stock location. Integrating artificial intelligence (AI) improves location estimation accuracy of up-to-date stock visibility. The ability of RFID pressure sensing tags and AI to detect stock from sensor code data enables more effective asset management, optimizes stock management, reduces product waste, and contributes to better efficiency in product inventory management. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Inventory control

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EDU-GAN:Edge Enhancement Generative Adversarial Networks with Dual-Domain Discriminators for Inscription Images Denoising

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computers, Materials & Continua 2024年第7期80卷 1633-1653页

作者： Yunjing Liu Erhu Zhang Jingjing Wang Guangfeng Lin Jinghong Duan School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an710048China Department of Information Science Xi’an University of TechnologyXi’an710054China School of Faculty of Painting Packaging Engineering and Digital MediaXi’an University of TechnologyXi’an710048China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China

Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research *** fromnatural images,character images pay more attention to stroke ***,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character *** solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,*** existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription ***,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising *** proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure *** to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image *** experimental results show the superiority of our method both in the synthetic and real-inscription datasets.

关键词： Dual-domain discriminators inscription images denoising edge-guided generator

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MiSC: A hybrid multi-modal deep learning approach for accurate skin cancer detection

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Multimedia Tools and Applications 2025年 1-25页

作者： Mohamed, Ensaf Hussein Abdu, Nada Khalil, Mostafa Kamal, Hossam Rashed, Essam A. Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Nile University Giza Egypt Graduate School of Information Science University of Hyogo Kobe650-0047 Japan Advanced Medical Engineering Research Institute University of Hyogo Himeji670-0836 Japan

With skin cancer rates increasing globally due to factors like prolonged ultraviolet exposure, early and accurate detection methods are crucial in managing and mitigating the disease. Implementing innovative diagnostic techniques can significantly enhance early intervention, improving patient outcomes and survival rates. This research aims to precisely identify and classify six skin cancer types. The proposed method integrates clinical images and metadata into a hybrid deep learning model, leveraging feature extraction and classification. The proposed approach employs seven machine learning algorithms alongside ten pre-trained deep learning models, notably featuring MobileNetV2 for image feature extraction, logistic regression for image classification, and random forest for metadata analysis. The developed approach is tested with the PAD-UFES-20 dataset and it demonstrate a notable improvement in diagnostic accuracy (95.6%), precision (96.8%), recall (95.6%), and F1-Score (95.7%). This findings highlight the significant contribution of metadata in enhancing classification accuracy, surpassing image-based methods alone. © The Author(s) 2025.

关键词： Deep learning

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