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Study of AI-Driven Fashion Recommender Systems

SN computer science 2023年第5期4卷 1-19页

作者： Shirkhani, Shaghayegh Mokayed, Hamam Saini, Rajkumar Chai, Hum Yan Department of Computer Science Electrical and Space Engineering Luleå Tekniska Universitet Luleå Sweden Department of Mechatronics and Biomedical Engineering Universiti Tunku Abdul Rahman Selangor Malaysia

The rising diversity, volume, and pace of fashion manufacturing pose a considerable challenge in the fashion industry, making it difficult for customers to pick which product to purchase. In addition, fashion is an inherently subjective, cultural notion and an ensemble of clothing items that maintains a coherent style. In most of the domains in which Recommender Systems are developed (e.g., movies, e-commerce, etc.), the similarity evaluation is considered for recommendation. Instead, in the Fashion domain, compatibility is a critical factor. In addition, raw visual features belonging to product representations that contribute to most of the algorithm’s performances in the Fashion domain are distinguishable from the metadata of the products in other domains. This literature review summarizes various Artificial Intelligence (AI) techniques that have lately been used in recommender systems for the fashion industry. AI enables higher-quality recommendations than earlier approaches. This has ushered in a new age for recommender systems, allowing for deeper insights into user-item relationships and representations and the discovery patterns in demographical, textual, virtual, and contextual data. This work seeks to give a deeper understanding of the fashion recommender system domain by performing a comprehensive literature study of research on this topic in the past 10 years, focusing on image-based fashion recommender systems taking AI improvements into account. The nuanced conceptions of this domain and their relevance have been developed to justify fashion domain-specific characteristics. © 2023, The Author(s).

关键词： Artificial Intelligence (AI) computer vision Content-based image retrieval (CBIR) Deep learning Fashion recommender system

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Circumventing Google Play vetting policies: a stealthy cyberattack that uses incremental updates to breach privacy

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4785-4794页

作者： Muhammad, Zia Amjad, Faisal Iqbal, Zafar Javed, Abdul Rehman Gadekallu, Thippa Reddy Department of Computer Science North Dakota State University Fargo United States Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan Islamabad Pakistan School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Today digital technologies are evolving to accommodate small businesses and young entrepreneurs by reducing their time-to-market while encouraging rapid innovation in mobile, Extended Reality (XR), Internet of Things (IoT), cloud, and edge devices. The leading operating system Android typically takes one to a few days to perform application vetting and go to production by leveraging code analysis technologies in their Play Protect anti-malware program. However, developers with malicious intent are looking to circumvent this detection mechanism by exploiting Google’s relatively lenient trust policies that allow for package distribution and feature updates. This paper develops a proof-of-concept malware that exploits customers’ trust and Google’s policies to circumvent popular voice search applications. Our results show that attackers can initially circumvent Play Protect by uploading benign applications to build trust and then add malicious feature updates incrementally to distribute highly intrusive malware into user systems. This malware can scan and collect private user data from the device and exfiltrate it to the command-and-control server. The contributions are three-fold. (1) A proof-of-concept stealthy malware and publishing mechanism has developed that highlights the relative ease with which Google Play Protect policies may be subverted. (2) a comprehensive evaluation has been performed using major publicly available anti-malware solutions. (3) Recommendations and policies have been suggested to prevent this attack and ensure users’ privacy concerns (IMUTA is a novel attack in which malicious functionality is slowly added to a benign application through updates. This attack evades malware detection tools and exploits user trust. The attack can be launched against any application distribution platform like the Play Store). © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile security

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Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC Converter

Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Wanyeki, Babuabel M. Boles, Jessica D. Lang, Jeffery H. Perreault, David J. Meta Platforms Inc New York CityNY United States University of California Berkeley Electrical Engineering and Computer Sciences BerkeleyCA United States Massachusetts Institute of Technology Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9798350316445

Magnetics pose a significant challenge for power converter miniaturization. Approaches to address this include power converters based on piezoelectric resonators (PRs) rather than magnetics, and switched-capacitor (SC) converters which utilize only capacitors. However, PR-based converter efficiencies tend to decrease at high voltage conversion ratios, and purely SC-based converters are not capable of efficiently regulating voltage. Through the hybridization of PR and SC-based converters, there is an opportunity for each to address the drawbacks of the other to create a high power density, high efficiency power converter for applications requiring high voltage conversion ratios and wide, continuous voltage conversion ranges. In this paper, we propose a two-stage dc-dc converter with a PR-based regulation stage and a SC-based transformation stage. We detail a new simplified eight-stage switching sequence for the PR stage and experimentally validate this concept in a prototype converter. © 2023 IEEE.

关键词： dc-dc power converters piezoelectric resonators switched capacitor converters switching sequences

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Multi-level Learning for Semi-supervised Multi-view Classification 11

Multi-level Learning for Semi-supervised Multi-view Classifi...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Song, Na Yang, Shundong Cai, Zhiling Lin, Jian Huang, Sujia Putian University School of Mechanical Electrical and Information Engineering Putian China Hainan University School of Computer Science and Technology Haikou China Fujian Agriculture and Forestry University College of Computer and Information Science Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350345346

Multi-view semi-supervised classification primarily aims to enhance classification accuracy when dealing with limited labeled samples. Although existing methods have shown impressive performance, significant challenges still persist in efficiently propagating label information and capturing global relationship information within multi-view data. To address the aforementioned challenges, a Graph Convolutional Network (GCN)-based framework is presented, which explores the acquisition of multilevel representations, encompassing feature-level representations and the fusion of global structural associations. The state-of-the-art performance of the proposed model is unequivocally supported by a comprehensive set of empirical results. © 2023 IEEE.

关键词： computer vision

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Impact of Parametric Uncertainties on the Steady-State Performances of DFIG-Based WECS 10th

Impact of Parametric Uncertainties on the Steady-State Perfo...

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10th EAI International Conference on Advancements of science and Technology, EAI ICAST 2022

作者： Ayenew, Endalew Biru, Getachew Mulatu, Asrat Berhanu, Milkias Electrical Power and Control Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Center of Excellence for Sustainable Energy College of Electrical and Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031411724

This chapter presents the study carried out to discover uncertain parameters in a model of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) and to investigate their effects on WECS plant while harvesting energy from wind at varying speeds. This is carried out by analytically formulating the steady-state models of a DFIG-based WECS and computing the parameters of these models at different wind speeds that drive wind turbines at rotor speed from sub-synchronous to super-synchronous. The main uncertain parameters in this system are the mutual reactances between stator and rotor windings, and the reactance of rotor winding. These variations happen due to the varying wind speed. The effect of parametric uncertainties on the steady-state performances of DFIG-based WECS is examined. The results depicted that the steady-state performances of DFIG-based WECS are influenced by uncertain phenomena. Typically a 0.5% change in mutual reactances caused up to 10 kW change in the generator output power. Mainly, it causes instability in the performances of the DFIG-based WECS and slightly reduces overall efficiency at a higher value of the wind speed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DFIG-based WECS HAWT Performances of WECS Uncertain parameter

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A Robust Security Method for Medical Image Based on 2D Schaffer Map 4

A Robust Security Method for Medical Image Based on 2D Schaf...

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4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Sharma, Suvita Rani Kaur, Manpreet Singh, Birmohan Minocha, Sachin Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur Punjab Longowal148106 India Bennett University SCSET Greater Noida India Sant Longowal Institute of Engineering and Technology Department of Electrical and Instrumentation Engineering Sangrur Punjab Longowal148106 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350395914

In this work, a methodology has been propounded for the security of the medical images containing Confusion and Diffusion phases in which a 2D Schaffer map is utilized. The confusion phase rearranges the row and column of medical images using the sorted indexes of the chaotic sequences produced by the Schaffer map. During the diffusion phase, the chaotic sequences are utilized to alter the pixel values of the original image through the XOR operation. This phase applies to both the row and column pixel values of the images, enhancing security. The efficacy of the proposed security method has been assessed through histogram analysis, chi-square, and variance test, revealing uniformity in encrypted image pixel values. The correlation analysis shows results closer to zero and an entropy value nearly equal to the ideal 8. Moreover, the encrypted image exhibits lower Peak Signal-to-Noise Ratio (PSNR) and higher Mean Square Error (MSE) values, indicating structural dissimilarity with the original image. The Unified Averaged Changed Intensity (UACI) value exceeds 33.40% and the Number of Changing Pixel Rate (NPCR) value exceeds 99.60%, indicating resistance against differential attacks, while the Structural Similarity Measure (SSIM) values closer to zero for the encrypted image indicates structural dissimilarity. The results affirm that the propounded methodology is secure from entropy, differential, statistical, and brute force attacks. © 2024 IEEE.

关键词： Image enhancement

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Building the metaverse using digital twins at all scales,states,and relations

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Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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Optimized Scheduling of Electric Vehicles Charging in Smart Grid using Deep Learning 4

Optimized Scheduling of Electric Vehicles Charging in Smart ...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Joshua, K. Paul Ranga, Jarabala Prasad, P. Venkata Mallala, Balasubbareddy Rajendiran, M. Maranan, Ramya SCAD College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Ramachandra College of Engineering Department of Electrical and Electronics Engineering Andhra Pradesh Eluru India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This research presents an integrated research framework for electric vehicle charging infrastructures under smart grids. It employs Genetic Algorithms and machine learning models such as Artificial Neural Networks (ANNs), Decision Trees, and Recurrent Neural Networks. The GA is able to minimally stack waiting times strategically and, by the effective allocation of charging points to drivers at various stages in their route, maximizes usage. In contrast, requiring intensive accurate training on massive data sets are the ML methods, which can also predict future demand for efficient use of resources. Results of the method demonstrate the strength of these techniques, with ANNs reaching nearly 99% accuracy. By applying this comprehensive methodology, operational metrics such as waiting time for EV owners are significantly improved and sustainable power management becomes more feasible. These findings underscore the potential for artificial intelligence to revolutionize energy distribution-so that it balances consumer convenience with tighter environmental targets and ways of working even now taking shape concerning electric vehicle charging. © 2024 IEEE.

关键词： Smart power grids

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Common-Mode Drift Resilient Ring-Oscillator-Based Time-Domain Filter for Next-Generation Wireless 66

Common-Mode Drift Resilient Ring-Oscillator-Based Time-Domai...

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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Xu, Qiuyan Lin, Chung-Ching Hut, Huan Gupta, Subhanshu School of Electrical Engineering and Computer Science Washington State University Pullman United States School of Microelectronics Southern University of Science and Technology China

ISBN: (纸本)9798350302103

Time-domain filters with single- and differential-ended architectures are technologically scalable and cost-efficient suited for next-generation wireless transmitters. Differential time-domain filters provide better linearity but are limited by common-mode drifts and other second-order effects. This paper provides detailed analysis of single- and differential-ended time-domain filter architecture for wireless transmitters with a solution to alleviate common-mode drift and achieve high linearity. © 2023 IEEE.

关键词： common-mode drift pseudo-differential time domain filter Voltage-controlled ring oscillator

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Enhancing Lung Cancer Diagnosis with Machine Learning Methods and Systematic Review Synthesis 8

Enhancing Lung Cancer Diagnosis with Machine Learning Method...

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8th International Conference on electrical, Electronics and Information engineering, ICEEIE 2023

作者： Khan, Razib Hayat Miah, Jonayet Nipun, Shah Ashisul Abed Islam, Majharul Amin, Md Shahedul Taluckder, Md Siam Independent University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of South Dakota Department of Computer Science SD United States North South University Department of Electrical and Computer Engineering Dhaka Bangladesh University of Tennessee Chattanooga Department Finance & Economics United States Lamar University Phillip M. Drayer Department of Electrical Engineering United States

ISBN: (纸本)9798350340501

Lung cancer is the leading cause of death in the USA It is caused for various reasons, such as malignant tumors that grow spontaneously in the lung and spread to other parts of the body, where they can be devastating. Other than that, there are so many external reasons why smoking is the primary factor that gradually creates lung problems that lead to lung cancer. However, we can prevent this deadly cancer by detecting it at an early stage. A machine learning-based detection model has the potential to detect lung cancer, which helps to predict the early stage of cancer. So, we aim to build some powerful machine learning algorithms to predict this cancer that can help physicians make decisions about the diagnosis process of a particular patient, whether the patient needs a high or a regular diagnosis level. This can reduce the treatment cost of a patient because the physician knows from the result how many doses are needed for a patient, so it can avoid unnecessary treatment, which saves money and brings an efficient diagnosis system. We aimed to build a sustainable model that could predict the disease accurately, and finally, our models, such as XGBoost, achieved the highest value of other models. According to the accuracy level, XGBoost performed up to 95.92%, LightGBM, AdaBoost, Logistic Regression, and Support vector machine achieved 91.50%, 93.32%, 57.41 %, and 89.32% accuracy, respectively. © 2023 IEEE.

关键词： Diagnosis

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