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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Arabelli, Rajeshwarrao Boddepalli, Eswararao Yadav, Roobal Kanwer, Budesh Wicaksono, Yudi Krisno Dhanraj, Joshuva Arockia Sr University School of Engineering Telanagana Warangal India Virginia Tech Bradely Department of Ece Blacksburg United States G.L. Bajaj Institute Of Technology And Management Greater Noida India Poornima Institute of Engineering and Technology India Uin Sayyid Ali Rahmatullah Social Science Education Program Tulungagung Indonesia Lovely Professional University Research and Development Cell Jalandhar-Delhi Grand Trunk Road Punjab Phagwara144411 India

ISBN: (纸本)9798350350067

The Internet of Things (IoT) has altered many fields because it combines so entirety. But likely that these instruments are so smooth to hack, it has still created plenty safety worries. To hold Wi-Fi IoT networks cautious, Intrusion Detection and Prevention Systems (IDPS) are very main. They look for and fix safety dents. This research focuses on Machine Learning (ML) systems and looks at the various types of IDPS for Wi-Fi IoT networks. There is plenty research on intrusion detection systems (IDS) over the internet of things (IoT) that is look at in this place study. It displays by virtue of what well differing algorithms, containing neural networks, decision trees, random forests, and Support Vector Machines (SVM), act. It further checks machine learning-based IoT safety models, like Support Vector Clustering (SVC) in addition to convolutional neural networks (CNNs), in consideration of label and stop hacking risks in IoT backgrounds. Reviewing secondary evidence and secondary data is part of the study process. These are used to find the basic IDPS for Wireless IoT Networks styles and questions. This research explains that intrusion detection systems (IDS) are essential for consistency the Internet of Things cautious, what plans established machine learning can help find and stop threats more effectively. This study has plenty useful information for scientists and experts that can help them devise better IDPS remedies for Wireless IoT Networks. © 2024 IEEE.

关键词： Network intrusion

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Design of Optical System of Augmented Reality Near Eye Device 6

Design of Optical System of Augmented Reality Near Eye Devic...

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6th IEEE Eurasian Conference on Educational Innovation, ECEI 2023

作者： Chang, Chao-Kai Yeh, Feng-Ming Lue, Jiann-Hwa Wang, Hsuan-Fu Hsieh, Ya-Hui Chiang, Chun-Yu Chen, Der-Chin Tien, Chuen-Lin Department of Optometry Yuanpei University of Medical Technology Hsinchu Taiwan Department of Optometry Taiwan University of Science and Technology Taichung Taiwan Department of Aeronautical Engineering National Formosa University Yunlin Taiwan Department of Healthcare Information and Management Ming Chuan University Taoyuan Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering Feng Chia University Taichung Taiwan

ISBN: (纸本)9798350334289

Augmented reality (AR) is a technology that instantly merges digital pictures with the real scene. The perspective near-eye display is a key module of AR, through which computer-generated pictures or videos can be displayed in the existing environment. The purpose of this research is to establish the system structure of an augmented reality near-eye display device and to design the off-axis two-piece reflective aspherical mirror. Because AR glasses are wearable and are worn close to the eyes, not only the optical performance of the display but also the clear vision of the user needs to be considered. In this study, an off-axis two-piece reflective aspherical mirror is used as the optical system of the AR for displaying the digital image. The micro-display is placed at the focal point of the optical system to form a digital image, and the horizontal movement of the micro-display along the optical axis is controlled by a precise displacement mechanism so that the patient with uncorrected myopia or hyperopia can see the digital picture and real scene. Because the right and left micro-displays move horizontally independently, the user can see the images on the AR device, which becomes the biggest feature of this study. © 2023 IEEE.

关键词： Augmented reality

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Observing super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate at an agro-forest site in Thailand

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Environmental science and Pollution Research 2024年第55期31卷 63718-63733页

作者： Bridhikitti, Arika Kumsawat, Chananphat Phitakpinyo, Nutthanaphat Sontisaka, Sirawich Naksaro, Ratipong Sawangproh, Weerachon Nakayama, Tomoki Environmental Engineering and Disaster Management Program School of Interdisciplinary Studies Mahidol University Kanchanaburi Campus 199 Moo 9 Lumsum Sub-District Saiyok District Kanchanaburi71150 Thailand Mahidol-York Interdisciplinary Living Research Excellence Center Mahidol University Kanchanaburi Campus 199 Moo 9 Lumsum Sub-District Saiyok District Kanchanaburi71150 Thailand Conservation Biology Program School of Interdisciplinary Studies Mahidol University Kanchanaburi Campus 199 Moo 9 Lumsum Sub-District Saiyok District Kanchanaburi71150 Thailand Faculty of Environmental Science Nagasaki University 1-14 Bunkyo-Machi Nagasaki Nagasaki City8528521 Japan

Coarse aerosol particles containing chloride in tropical forests are significant for understanding biogeochemical cycles and atmospheric processes, with implications for environmental health and climate change mitigation. This study explored the sources of super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate. Aerosol samples were collected from an agro-forest site in Thailand during the dry season and analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy. By examining the morphology and elemental compositions of individual aerosol particles, along with employing Positive Matrix Factorization (PMF) and backward trajectory analysis, potential sources were identified. The findings revealed two primary sources for the super-coarse aerosol particles: a mixture of biomass burning smoke and inorganic salts (likely from saline soil and sea salt), as well as halophilic fungal spores. FTIR analysis indicated the presence of compounds linked to biomass burning and clay minerals, influenced by prevailing northeast and southeast winds. Recommendations for future research include continued monitoring, correlation with meteorological parameters, and the application of transmission electron microscopy (TEM) for more detailed visualization and confirmation of aerosol sources. © The Author(s) 2024.

关键词： Fourier transform infrared spectroscopy

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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International Conference on ICT For Smart Society (ICISS)

作者： Desyra Sukma Dewanthi Hari Kristopo Nunung Nurul Qomariyah Muhammad Axel Management Department Business Management & Marketing Program BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18–40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business.

关键词： Data privacy Privacy Consumer behavior Focusing Electronic commerce Security Artificial intelligence Sustainable development Interviews Recommender systems

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Spatio-Temporal Change of Habitat Quality in Northeast China: Driving Factors Exploration Based on Land Use and Land Cover Change

SSRN

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SSRN 2024年

作者： Wu, Chuanbao Cui, Yingying Zhuang, Xiaowen Huang, Guohe College of Economics and Management Shandong University of Science and Technology Qingdao266590 China Environmental Systems Engineering Program Faculty of Engineering University of Regina ReginaSask. S4S 0A2 Canada

Although the ecological environment plays an indispensable role in sustaining and developing human society and natural ecosystems, it continually suffers from degradation caused by human activities. Land Use and Land Cover (LULC), which serves as a proxy of the intensity for human intervention, has been regarded as an equally important factor affecting habitat quality as climate change. Therefore, exploring the close relationship between LULC and habitat quality change would provide a scientific basis for ecological conservation and land use. In this study, taking Northeast China with prominent contradiction between LULC and ecological protection as the study area, the InVEST model was introduced to assess the habitat quality based on LULC data from 2000 to 2020. And then, the Geographically Weighted Regression (GWR) model was employed to analyze the explanatory variables of habitat quality change in terms of LULC change. The results indicated that LULC change in Northeast China from 2000 to 2020 mainly occurred between cultivated land, artificial land, grassland, and forestland. Habitat quality demonstrated a progressive decline yet remained at an intermediate level, and exhibited significant spatio-temporal heterogeneity on the whole. Furthermore, the regression results demonstrated there was a significant correlation between LULC change and habitat quality change. Finally, Northeast China was classified into three functional zones by the K-Means clustering analysis: the coordinated development zone, ecological conservation zone and key ecological functional zone, each with its own characteristics and development priorities. The findings can provide a scientific reference for the rational use of land and zoning management of habitat quality in Northeast China. © 2024, The Authors. All rights reserved.

关键词： Land use

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Hand-Woven Fabric Motif Recognition Methods: A Systematic Literature Review 3

Hand-Woven Fabric Motif Recognition Methods: A Systematic Li...

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Noprisson, Handrie Ermatita, Ermatita Abdiansah, Abdiansah Ayumi, Vina Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Knowledge of hand-woven motif recognition is only owned by the older generation, which has not been passed down to the younger generation. Moreover, computer technology can be used to support the recognition of traditional woven fabric motifs. The aim of this research is to conduct a literature review on hand-woven fabric motif recognition, with an emphasis on performance accuracy, techniques, and datasets utilized. This research included three distinct stages of systematic literature review (SLR): planning, execution, and reporting of findings. We found several datasets between 924,845 data and the number of classes between 3-25 class. Based on research result, we obtained several methods for hand-woven fabric motif recognition focused on performance examination. We recommended image pre-processing method, including Adaptive Filtering Denoising, Adaptive Wiener Filtering, Histogram Equalization, Gradient Pyramid (GP) Decomposition. Moreover, we suggested five feature extraction methods, including Radon Transform, Wavelet Transform, Locally Rotation Invariance Measure (LBP-ROR), Transform Invariant Low-Rank Textures (TILT) and Histogram of Oriented Gradients (HOG). For learning method, we recommend Fuzzy C-Mean (FCM), Convolutional neural network (CNN), Deep Neural Network (DNN), MobileNets, Inception-v3 and ResNet-50. © 2021 IEEE.

关键词： Weaving

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A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments

A Secure Cloud-Based Infrastructure for Virtual Sensors in I...

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Women in Data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Tahira Jehan Makda Ana Luiza Barros Selma Dilek College of Business Management University of Doha for Science and Technology Doha Qatar Computer Science Program State University of Ceará Fortaleza Brazil Department of Computer Engineering Hacettepe University Ankara Turkey

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology.

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Davide Mascolo Lorenzo Farina Manuela Petti MSc Program in Data Science Sapienza University of Rome Rome Italy Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy.

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Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

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Computers and Industrial engineering 2024年 189卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Chang, Yung-Sheng Chang, Chun-Hsiang Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

In smart factories, automated material handling system (AMHSs) replace manual material handling to increase production efficiency, in which stockers serve as the temporary storage for work-in-process inventories. Furthermore, costly machines and complex processes in advanced manufacturing lead to the need for multiple reentrant processes of the same workstation. However, previous works on scheduling problems rarely considered the function of stockers and reentrant processes, and their approaches were mostly based on metaheuristic algorithms. Recent advances in artificial intelligence enable the possibility of solving scheduling problems using deep reinforcement learning (DRL). Therefore, this work investigates the reentrant hybrid flow shop scheduling problem with stockers (RHFS2) inspired by AMHSs, and solves it by DRL. Firstly, the states, actions, and rewards of the Markov decision process for this problem are designed;and then, two deep Q network (DQN) approaches based on the actions for determining machines and jobs, respectively, are proposed. Simulation results demonstrate that our proposed DQN approaches outperform for finding better solutions of different-scale problems than classical metaheuristic algorithms. © 2024 Elsevier Ltd

关键词： Reinforcement learning

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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Information Technology and Computing (ICITCOM), International Conference on

作者： Debby Alita Imam Ahmad Rhaishudin Jafar Rumandan Moh Erkamim Widyasmoro Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Islamic Education Management Study Program Institut Agama Islam Negeri Ambon Ambon Indonesia Smart City Information Systems Study Program Universitas Tunas Pembangunan Surakarta Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350379839

ISBN: (纸本)9798350379846

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data.

关键词： Pediatrics Accuracy Computational modeling Prevention and mitigation Data models Classification algorithms Decision trees Data mining Information technology Diseases

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