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A Systematic Literature Review of Food Recommender Systems

SN computer science 2024年第1期5卷 174页

作者： Mahajan, Pratibha Kaur, Pankaj Deep Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India Computer Science and Engineering Department Guru Nanak Dev University Regional Campus Jalandhar India

The enormous variations in food choices and lifestyle in today’s world have given rise to the demand of using recommender system as a suitable tool in making appropriate food choices. Need for choosing nutritious food items is becoming important in today’s modern lifestyle as people are getting indulged in eating unhealthy food and thus leading to miserable health status. Many lifestyle-related diseases, such as diabetes and obesity, can also be reversed by proper diet. Filtering techniques in recommender system use a dataset of items and user’s preferences as input to discover a list of well-judged items as suggestions. In light of the above said, this paper presents an exploratory study of the recommendation approaches and methods utilized for food and recipe recommendations in the past decade. For this purpose, several relevant papers published in the food domain from 2006 to 2023 have been extensively studied. Further, the research articles are categorized based on the filtering techniques, methods, functionalities and data sources used by the researchers. A comparative study of the recommendation approaches revealed the advantages and disadvantages of different approaches. Also, the paper emphasizes the importance of food recommendation techniques in health and nutrition management of an individual. The review highlights the need to further explore the implementation of recommender systems in the food industry. Furthermore, the findings of this survey provide researchers with insights and future directions on recommender systems in the domain. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Food Food/Recipe/Menu recommender system Information retrieval Menu Recipe Recommender system

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Motion Error Estimation and Coherent Integration for High-Speed Target With Airborne Bistatic Radar

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IEEE Transactions on Aerospace and Electronic Systems 2024年第3期61卷 5699-5717页

作者： Li, Xiaolong Zhang, Zerui Yang, Fan Wang, Mingxing Yeo, Tat Soon Cui, Guolong University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore

With flexibility in maneuverability and remarkable adaptability, airborne bistatic radar system can obtain excellent detection performance for high-speed target by employing coherent integration. However, range migration (RM) and Doppler frequency migration (DFM) could become serious issues due to the relative motion characteristics of airborne platforms and high-speed target. Meanwhile, various unpredictable factors such as atmospheric turbulence and mechanical issues, etc., resulting in additional motion errors, would have further negative impacts on motion state and flight trajectory of airborne platforms. This phenomenon would serious consequence on coherent integration and target detection. Thus, we make contributions to tackle these limitations and enhance coherent integration and detection performance. First, we establish signal model with high-speed target in three-dimensional (3-D) space for airborne bistatic radar system, along with motion error model which simultaneously includes translational error and rotational error. Next, we articulate range history's mathematical expression and further derive echo signal model. We then propose an improved generalized Radon Fourier transform (IGRFT) method. More specifically, the purpose of IGRFT is achieving joint search for the parameters of the target motion and the parameters of motion error, to ensure high precision parameter estimation and high gain integration. However, the computational complexity surges due to the increasing of search dimensionality. To devise computationally feasible methods for practical applications, we split the high-dimensional maximization process into two disjoint problems by sequentially searching motion parameters and then motion error parameters, and this method is named GRT (generalized Radon transform)-IGRFT. Numerical simulations show that the proposed algorithms can correctly estimate parameters and achieve signal integration and target detection. Finally, we present performanc

关键词： Flight paths

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Broadband spin and angle co‑multiplexed waveguide‑based metasurface for six‑channel crosstalk‑free holographic projection

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eLight 2024年第1期4卷 223-237页

作者： Zeyang Liu Hao Gao Taigao Ma Vishva Ray Niu Liu Xinliang Zhang L.Jay Guo Cheng Zhang School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Physics University of MichiganAnn ArborMI 48109USA Lurie Nanofabrication Facility Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborMI 48109USA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA

Metasurface-based holograms,or metaholograms,offer unique advantages including enhanced imaging quality,expanded field of view,compact system size,and broad operational ***-channel metaholograms,capable of switching between multiple projected images based on the properties of illuminating light such as state of polarization and angle of incidence,have emerged as a promising solution for realizing switchable and dynamic holographic ***,existing designs typically grapple with challenges such as limited multiplexing channels and unwanted crosstalk,which severely constrain their practical ***,we present a new type of waveguidebased multi-channel metaholograms,which support six independent and fully crosstalk-free holographic display channels,simultaneously multiplexed by the spin and angle of guided incident light within the glass *** employ a k-space translation strategy that allows each of the six distinct target images to be selectively translated from evanescent-wave region to the center of propagation-wave region and projected into free space without crosstalk,when the metahologram is under illumination of a guided light with specific spin and azimuthal *** addition,by tailoring the encoded target images,we implement a three-channel polarization-independent metahologram and a two-channel full-color(RGB)***,the number of multiplexing channels can be further increased by expanding the k-space’s central-period region or combing the k-space translation strategy with other multiplexing techniques such as orbital angular momentum *** work provides a novel approach towards realization of high-performance and compact holographic optical elements with substantial information capacity,opening avenues for applications in AR/VR displays,image encryption,and information storage.

关键词： Metasurface Multi-channel metahologram Color metahologram Waveguide display

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Preprocessing with Skin Segmentation to Improve Monk Skin Tone (MST) Classification 11

Preprocessing with Skin Segmentation to Improve Monk Skin To...

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11th International Conference on electrical engineering, computer science and Informatics, EECSI 2024

作者： Alifia, Rizka Vanissa Ayu, Media Anugerah Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350355314

Skin tones come in a diverse range of shades and are often necessary for various computer vision tasks. While skin detection is a well-studied focus, skin tone classification is not. Most works also use the Fitzpatrick Skin Type (FST) scale despite it being developed for skin phototype classification purposes. Due to the lack of a proper skin tone scale, the Monk Skin Tone (MST) was created, which was used to perform skin tone classification using Convolutional Neural Network (CNN) model as well as the Individual Topology Angle (ITA) value threshold method with skin segmentation as a preprocessing for both classification methods. To investigate if skin segmentation can improve classification accuracy, the MST classification was performed using both segmented and unsegmented images involving the IT A value threshold method as well as two CNN models that were trained using different image types. The experimental results showed that the best way to perform MST classification was using a CNN model trained and tested with segmented images, which yielded the highest accuracy score being 0.91, with no signs of over or underfitting. The results also supported that segmentation is necessary, and that it can improve a classification model's accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

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IET Cyber-Systems and Robotics 2024年第2期6卷 20-34页

作者： Tony X.Lin Jia Guo Said Al-Abri Fumin Zhang School of Electrical&Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA College of Engineering Sultan Qaboos UniversityMuscatOman Department of Electronic&Computer Engineering Hong Kong University of Science and TechnologyHong KongChina

The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high *** computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP *** approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the *** approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.

关键词： environment sensing multi-robot systems

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A Systematic Review of Federated Learning Mechanism for The Internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the Internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Image-Based Automatic Energy Meter Reading Using Deep Learning

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computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad Imran Hafeez Anwar Muhammad Tufail Abdullah Khan Murad Khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

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