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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

引用

Electronics and Renewable systems (ICEARS), International conference on

作者： Iyyanar Perumal Kalaivani P Naveenkumar A Saran V Sriram K Suruthi N Department of Information Technology Sona College of Technology Salem India Department of CSE Kongu Engineering College Perundurai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study investigates the utilization of artificial intelligence (AI) to transform skincare product recommendations and the formulation of individualized routines through the analysis of user inputs and characteristics. The system utilizes data including skin type, acne count, oiliness level, and UV exposure, using KNearest Neighbors (KNN) for personalized product suggestions. Natural Language processing (NLP) approaches are utilized to develop tailored 7-day skincare regimen recommendations by examining product and user problems, converting unstructured data into actionable insights. Moreover, deep learning algorithms are employed for real-time image-based assessments of skin conditions, facilitating precise acne diagnosis and oiliness evaluation. The system integrates visual data with textual analysis, continually adapting through user feedback to improve accuracy and relevance. This article highlights the capacity of AI-driven methods to automate routine generation and provide tailored product recommendations, fostering innovation in the cosmetics sector through a thorough, data-oriented strategy.

关键词： Deep learning Visualization Accuracy Transforms Nearest neighbor methods Skin Natural language processing Real-time systems Artificial intelligence Wearable devices

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Dynamic optical Model for Time-Based Enhancement of Augmented Reality Environments

Dynamic Optical Model for Time-Based Enhancement of Augmente...

引用

IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Qi Zhang School of Information Technology Illinois State University Normal Illinois United States

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Dynamic lighting is essential for creating realistic day-night cycles and enhancing augmented reality (AR) experiences. In this paper, we introduce a dynamic optical model that simulates the natural progression of light from sunrise to sunset and into nighttime, bringing environments to life. Our model leverages six photographic images as backgrounds for AR scenes and incorporates advanced rendering techniques to generate textured models and geometries. These textures are enhanced using novel image processing methods to improve contrast and visibility, particularly in low-light conditions. Our algorithm ensures a seamless blend between virtual elements and the natural world by matching AR lighting conditions with the real environment. The environment's light transitions dynamically, from daylight brightness to ambient nighttime light, with the sun acting as the primary light source, rotating around the virtual world to simulate realistic lighting and shading effects. This texture-sampling-based lighting algorithm adapts to real-time conditions, interactively adjusting the lighting to enhance AR elements, highlight critical information, and create immersive, believable experiences in virtual simulations and AR applications.

关键词： Visualization Adaptation models Computational modeling Heuristic algorithms Lighting Virtual environments Rendering (computer graphics) optical imaging Real-time systems optical sensors

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Enhancing Arabic Sentiment Analysis Through Machine Learning and Deep Learning Techniques

Enhancing Arabic Sentiment Analysis Through Machine Learning...

引用

IEEE SSD International Multi-conference on systems, Signals and devices

作者： Abeer A. K. Alharbi Information Systems Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMISIU) Riyadh Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This study investigates the effectiveness of various machine learning techniques for sentiment analysis, focusing on data from multiple social media platforms. A comparative analysis was performed between traditional machine learning algorithms such as Naïve Bayes (NB), Support Vector Machines (SVM), and Random Forest (RF), alongside the Long Short-Term Memory (LSTM) recurrent neural network. Our results demonstrate that the LSTM approach significantly outperforms the traditional methods, achieving a classification accuracy of $\mathbf{9 2. 1 3 \%}$ . The research provides foundational insights for future studies in Arabic sentiment analysis, particularly in understanding consumer behavior across different sectors. The adopted methodology for extracting and analyzing consumer opinions emphasizes the critical role of data preprocessing in improving dataset quality and system accuracy. Limitations identified include the misclassification of neutral sentiments and the need for a multi-class classification framework. Future work aims to broaden the dataset to over 25,000 posts and explore multilingual sentiment analysis, addressing the complexities of classifying Arabic and English posts while expanding the model's application to additional natural language processing tasks.

关键词： Support vector machines Sentiment analysis Consumer behavior Accuracy Social networking (online) Data preprocessing Multilingual Bayes methods Long short term memory Random forests

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Dcha: Distributed-Centralized Heterogeneous Architecture Enables Efficient Multi-Task processing for Smart Sensing

Dcha: Distributed-Centralized Heterogeneous Architecture Ena...

引用

Design, Automation and Test in Europe conference and Exhibition

作者： Erxiang Ren Cheng Qu Li Luo Yonghua Li Zheyu Liu Xinghua Yang Qi Wei Fei Qiao School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China MakeSens AI Beijing China College of Science Beijing Forestry University Beijing China Department of Precision Instrument Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed.

关键词： Performance evaluation Energy consumption Soft sensors Scalability Collaboration Computer architecture Sensors Partitioning algorithms Internet of Things Artificial intelligence

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

引用

Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry Sensor phenomena and characterization Sensor systems Real-time systems Gas detectors Resilience

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A System for Controlling the GUI by Gestures using a Webcam, Based on MV algorithms

A System for Controlling the GUI by Gestures using a Webcam,...

引用

International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Nikita S. Borisov Yuri S. Belov Information Technology Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This article presents a gesture-controlled computer system using a webcam and machine vision (MV) algorithms, which allows for more intuitive and accessible human-computer interaction. The research is of great importance because it improves the usability of a computer for people with disabilities and offers a faster alternative to traditional input methods in limited conditions, such as the workplace. The goal is to develop a system that allows users to control graphical user interfaces (GUIs) using gestures using computer vision techniques. The technique includes image processing, hand segmentation, gesture recognition, and command execution. The experiments were conducted on Windows 10 and macOS. The results show successful gesture recognition and command execution in Windows 10, while macOS testing revealed the potential for simplification with built-in features. The study concludes that gesture control technology has significant potential to improve human-computer interaction, especially in environments where traditional input devices are impractical or unavailable.

关键词： Human computer interaction Electric potential Webcams Operating systems Input devices Gesture recognition People with disabilities Usability Testing Graphical user interfaces

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Liang Sheng Zhang Jie Wu School of Computer and Electronic Information and the School of Artificial Intelligence Nanjing Normal University State Key Laboratory for Novel Software Technology Nanjing University Center for Networked Computing Temple University US

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach.

关键词： Accuracy Image edge detection Visual analytics Signal processing algorithms Streaming media Signal processing Real-time systems Servers Speech processing Optimization

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A High-Density Electromyography Sensor Based on MEMS E-Skin for Detecting Activity of Motor Units

A High-Density Electromyography Sensor Based on MEMS E-Skin ...

引用

IEEE International conference on Micro Electro Mechanical systems

作者： Yirong Wang Shuihan Shao Chunpeng Jiang Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper introduces a compact, ultrathin, and stretchable high-density surface electromyography (sEMG) electrode designed for targeted signal acquisition from 16 or more channels in localized muscle regions around the mouth and neck during swallowing. These signals are processed into motor unit potentials through advanced decomposition algorithms. The electrode features a serpentine structure approximately 6 micrometers thick, enabling conformal contact with the skin and accommodating cyclic stretching up to 160%, thereby minimizing motion artifacts and ensuring high-quality signal capture. Surface modifications with iridium oxide and platinum black are applied to reduce contact impedance. The high channel density and small form factor facilitate detailed analysis of deep motor unit activity in targeted muscles using high-density sEMG decomposition techniques.

关键词： Electrodes Micromechanical devices Surface impedance Electronic skin Signal processing algorithms Muscles Motors Electromyography Impedance Surface treatment

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RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking Dataset And A Benchmark

RADCI: A Synchronized Radar-RGBT Object Detecting-Tracking D...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Heng Yu Ruiheng Zhang Haoyang Sun Zhe Cao Biwen Yang Jin Zhang Guanyu Liu Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

High-quality perception is crucial in autonomous driving and monitoring systems, where millimeter-wave radar and infrared cameras play important roles due to their robustness and reliability under harsh conditions. Both technologies can serve as low-cost supplements to optical image detection, improving overall system robustness. However, there is currently a lack of widely applicable feature-level fusion methods and multimodal datasets to effectively integrate visible light with these two heterogeneous data types for multiple tasks. In this work, we collect a new multimodal dataset, RADCI8, which synchronizes data from a camera, an infrared camera, and a radar for target detection and tracking. The dataset includes 2D image annotations, radar RAD tensor data with distance, angle, and Doppler information, as well as target ID annotations in both data formats. In addition, to address the incomplete use of radar data in previous fusion algorithms, we propose a detection method that fuses image and radar features using feature concatenation and an attention mechanism. Our proposed algorithm achieves 51.5% AP with an IOU of 50:95 on 2D bounding box prediction, significantly improving average detection accuracy over vision-based methods and maintaining robustness even when a single sensor degrades.

关键词： Tensors Radar detection Signal processing algorithms Radar imaging Radar tracking Cameras Prediction algorithms Robustness Doppler radar Synchronization

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Photonic Integrated Terahertz Imager

Photonic Integrated Terahertz Imager

引用

International Workshop on Mobile Terahertz systems (IWMTS)

作者： Thomas Haddad Mohammad Faraz Abdullah Niklas Schulz Andreas Stöhr Martin R. Hofmann Sascha Preu Optoelectronics Universität Duisburg-Essen Duisburg Germany Terahertz Devices and Systems Technische Universität Darmstadt Darmstadt Germany Photonics and Terahertz Technology Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

This scientific paper is an outcome of the MARIE collaborative research center (for more information, see ***) project C07. The paper discusses the development of photonic integrated circuits (PICs) for THz imaging in the WR3.4 band. First, a THz transmitter using a MUTC-PD with a saturation THz output power of ~500 μW at 0.3 THz is presented. In addition, a novel 2D photonic THz beam steering chip technology are presented using a LWA array with either a THz beamforming network or an optical beamforming network (OBFN) based on SiN/SiO2. Experimentally, maximum steering angles up to 62° and 39° are achieved for 1D and 2D THz beam steering, respectively. Second, the paper discusses InGaAs photoconductor receiver arrays integrated with OBFN-PICs. Furthermore, two dual-wavelength PICs are demonstrated, each is optimized for a large continuous tune range of the difference frequency or fast difference frequency modulation.

关键词： Phased arrays Terahertz wave imaging Beam steering Array signal processing optical saturation optical receivers optical transmitters optical sensors Terahertz communications Photonics

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