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Facial Shape Analysis and Accessory Recommendation: A Human-Centric AI Approach 3

Facial Shape Analysis and Accessory Recommendation: A Human-...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Komatireddy, Sanjay Reddy Meghana, Karnam Gude, Venkataramaiah Ramesh, G. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India GP Technologies LLC United States

ISBN: (纸本)9798350343632

In this digital world, the fusion of Artificial intelligence (AI) and Human-Computer Interaction (HCI) opens new avenues for enhancing user experiences through personalization. This research centers on the realm of facial shape analysis and its applications in providing tailored accessory recommendations, harnessing AI techniques such as Machine Learning (ML) algorithms to offer customized suggestions. The primary objective is to create an intelligent system adept at recognizing facial shapes and delivering accessory recommendations that harmonize seamlessly with an individual's unique facial characteristics. To accomplish this, advanced Artificial intelligence (AI) algorithms specialized in facial shape recognition and analysis have been utilized. These algorithms include K-Means, DBSCAN, and Agglomerative Clustering, each playing a crucial role in the process. The system's recommendations are founded on a comprehensive analysis that combines both quantitative data and qualitative insights. The findings underscore the effectiveness of AI-driven facial shape analysis in personalizing accessory recommendations. This study highlights AI's capacity to elevate user experiences and showcases the intersection of technology and fashion within HCI. As a contribution to the evolving field of AI and HCI, this research emphasizes the importance of user centered solutions. Moreover, the results act as a foundational pillar for future research in personalized recommendations and user interfaces, effectively bridging the gap between AI advancements and human-centric design. © 2023 IEEE.

关键词： Accessory Recommendation Artificial intelligence Facial Shape Analysis Human-Computer Interaction User-Centric Solutions Virtual Try-on

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Modeling and Nonlinear Coordinated Stabilizing Control for Dual Ship-Mounted Cranes Subject to Roll and Heave Motions 17th

Modeling and Nonlinear Coordinated Stabilizing Control for D...

引用

17th international conference on Intelligent robotics and applications

作者： Yang, Ling Ma, Xin He, Xin Zhang, Lei Yang, Xue Shandong Univ Sch Control Sci & Engn Engn Res Ctr Intelligent Unmanned Syst Ctr RobotMinist Educ 17923 Jingshi Rd Jinan 250061 Peoples R China Shandong Marine Grp Ltd 7000 Jingshi Rd Jinan 250022 Peoples R China Shandong Marine Grp Technol Co Ltd 300 Changjiang Rd Yantai 264006 Peoples R China

ISBN: (纸本)9789819607976;9789819607983

Wave-induced ship heave and roll motions pose great challenges for the design of control systems of dual ship-mounted cranes (DSCs) along with complicated nonlinearities and physical constraints. Aiming at the above issues, this paper establishes the dynamic model of DSCs subject to ship roll and heave motions and proposes a nonlinear coordinated stabilizing controller which could achieve precise positioning and swing-elimination. First, the DSCs dynamic model considering ship roll and heave motions is built by utilizing the Lagrange method. On the basis of the dynamic model, the system energy function consisting of the kinetic and potential energy is introduced. With the first-order derivation of the energy function, explicit expressions of ship-to-ship interaction forces caused by ship motions are derived. These forces are then incorporated into the controller to mitigate the effects between the two ships, thereby achieving coordinated control of the two cranes during payload transportation. Lyapunov techniques are used to rigorously prove the stability of the control system. Finally, numerous simulations verify the effectiveness of the proposed controller.

关键词： Dual ship-mounted crane systems Boom crane Nonlinear systems Motion control Wave disturbances

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Cybercheck - OSINT & Web Vulnerability Scanner 2

Cybercheck - OSINT & Web Vulnerability Scanner

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2nd international conference on Edge Computing and applications, ICECAA 2023

作者： Shamunesh, P. Vinoth, S. Srinivas, L.N.B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350347579

The objectives of 'CyberCheck' are drafted very precisely to solve 2 important modules of the Penetration Testing Workflows. Open-Source intelligence (OSINT) and Web Vulnerability Scanning are the most crucial practices for a penetration tester to keep track of their target and not to leave any of their digital footprints in the process of scanning their endpoints. Often, a pen-tester must be dependent on a 3rd party service provider, those who provide all open-source tools to check on. But this stage is not always beneficial for a pen-tester to have a fully transparent scanning on their target. The idea is to develop an Open-Source user-customizable OSINT and Web Vulnerability scanner in Python environment scripts that are very transparent on the algorithm for performing OSINT search or Web Vulnerability scanning. This feature is a nightmare for any pen-tester to test or track on their target endpoints using open-source that is highly transparent in scanning ports and functions that they trust on. This also adds the pen-tester to work ethically by customizing the type and parameter he wants to scan instead of going unethical in an online 3rd party service provider. © 2023 IEEE.

关键词： Clickjacking Enumeration OSINT (Open-Source intelligence) Penetration Testing Vulnerability Assessment Web Vulnerability

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BCI Controlled robot for Drones 3

BCI Controlled Robot for Drones

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3rd international conference on Smart Technologies, Communication and robotics, STCR 2023

作者： Shanmuga Priyan, M. Arunachalam, A.R. Dharneesh, S.K. Tamilselvan, S. Bannari Amman Institute of Technology Ece Tamilnadu India

ISBN: (纸本)9798350370867

There is a significant progress in the field of brain computer Interface (BCI) technology in recent days which allows communication between human brain and external devices. This BCI technology can be used to control robotic systems such as drones using the power of thoughts. This BCI controlled robot aims to enable users to control a drone through their brain signals which provides more intuitive and efficient means of interaction. It includes many components like BCI hardware, signal processing techniques etc. The BCI hardware is responsible for acquiring the user's brain signals and converting them into digital signals that can be processed by a computer. Electroencephalography is a technique used to measure and record the electrical activity of the brain. The electrodes are placed on the scalp to detect the electric signals produced by the neurons in the brain. The meaningful information from the acquired brain signals are extracted using signal processing algorithms. These algorithms analyze the signals and detect relevant patterns and translate them into commands for drones. The robotic drone platform includes the drone itself with communication modules. The control commands generated by the BCI system are transmitted wirelessly to the drone which enables controlling the drone. This system has numerous potential applications including searching and rescue operations and surveillance. This BCI controlled robot for drones mainly highlights the integration of BCI technology with robotic systems to enable direct control of drones using brain signals. © 2023 IEEE.

关键词： Brain computer interface

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Prediction Model of Horizontal Drilling Trajectory of Anti-collision Drilling robot 3

Prediction Model of Horizontal Drilling Trajectory of Anti-c...

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2023 3rd international conference on Artificial intelligence and Industrial technology applications, AIITA 2023

作者： Kang, Mingxia Wu, Honglin Hua, Dezheng School of Mechatronic Engineering China University of Mining Technology China

The horizontal drilling trajectory of the anti-collision drilling robot is taken as the research object and its drilling trajectory prediction model is established in this paper. The drilling trajectory is affected by many factors, such as the structural parameters of the auger, the drill bit, the drilling process parameters, and the geological characteristics of the coal seam, etc., which results in the existence of multi-level time-scale structure and localization characteristics of the drilling trajectory data in the time domain. The wavelet analysis method can decompose the relatively complex non-stationary time series problem into low-frequency decomposition signals representing trend items and high-frequency decomposition signals representing periodicity and randomness. It can not only predict the main trend of drilling trajectory changes but also effectively predict sudden load changes and short-term changes. Based on this, a new prediction method is proposed in this paper. Firstly, the data is decomposed into relatively simple component signals using wavelet decomposition technology, then the prediction models are established according to the characteristics of each component signal. Finally, the drilling trajectory prediction model is established by synthesizing the prediction results. The anti-impact drilling robot drilling test bed is set up and the drilling experiments are carried out to verify the effectiveness of the model, which can lay the foundation for the anti-impact drilling robot drilling deflection control. © Published under licence by IOP Publishing Ltd.

关键词： Trajectories

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Machine Learning based Intelligent Diagnosis of Orthodontics: A comprehensive review 3

Machine Learning based Intelligent Diagnosis of Orthodontics...

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3rd IEEE international conference on Computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Iwin Thanakumar Joseph, S. Sarveshwaran, Velliangiri Vijayakumar, K. Raj, S Benson Edwin Elngar, Ahmed A Devadurai, Anand Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Guntur India SRM Institute of Science and Technology Kattankulathur Campus Department of Computer Science and Engineering Chennai India Higher Colleges of Technology Department of Computer Information Sciences Fujairah United Arab Emirates Beni-Suef University Faculty of Computers and Artificial Intelligence Beni-Suef City Egypt King Khalid University Department of Computer Science Gregar Abha Saudi Arabia

ISBN: (纸本)9798350338263

An effective prediction of orthodontics treatment outcome is highly essential for further scheme of clinical treatment for the patient with the simplified cost. An appropriate utilization of information technology simplifies the treatment for teeth irregularities and misaligned jaws in an effective manner. Lot of research works carried out in the area of dentistry to come out with an optimistic result. With the advent of artificial intelligence and machine learning methodologies in recent years, lot of clinical problems and difficulties can be efficiently managed and overcome by orthodontists. This research article provides the complete insight about the impact of intelligent technologies in the field of orthodontics. It provides a complete overview of futuristic applications in the field of orthodontic clinical diagnosis, planning of treatment for various cases and the correct prediction of outcome of clinical treatment which regularly faced by an orthodontist in the health care sector. © 2023 IEEE.

关键词： Dentistry

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A Chinese short-text oriented intent classification network based on BERT and Bi-GRU 3

A Chinese short-text oriented intent classification network ...

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3rd IEEE international conference on Information technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Ma, Xiaolin Chen, Yufei Liu, Xinhua Kuang, Hailan Wuhan University of Technology School of Information Engineering Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan430070 China

ISBN: (纸本)9781665490788

With the development of computer technology, Internet applications have penetrated into all aspects of people's lives, and now people are more inclined to get answers from these applications when they encounter problems. To accurately answer users' questions, the prerequisite is to be able to accurately identify the intended category of users' questions. In this regard, this paper proposes a short textual intent classification network BERT-DBGRU based on BERT and Bi-GRU, which uses BERT as the word embedding layer and a two-layer Bi-GRU to complete the contextual feature extraction of the input sequence. Also, in order to utilize the features of the word vectors obtained from BERT as much as possible, the model also uses residual connectivity to fuse the word vector features with the output of the GRU network, after which the classification task is completed using the maximum pooling layer and the fully connected layer. After experimental verification, the accuracy of the method in this paper reaches 83.44% on the text classification task, and the effectiveness of this method on the text classification task is proved by comparing with classical networks such as TextCNN, TextRCNN. © 2023 IEEE.

关键词： Embeddings

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3rd international conference on Optimization, Learning Algorithms and applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, Learning Algorithms and applications, OL2A 2023

ISBN: (纸本)9783031530357

The proceedings contain 26 papers. The special focus in this conference is on Optimization, Learning Algorithms and applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as Image for AI to Recognize Objects in the Mobile robot Operational Environment;an Evaluation of Image Preprocessing in Skin Lesions Detection;an Artificial intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile robots;movement Pattern Recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using Image Processing and Machine Learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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Chat Generative Pretrained Transformer (ChatGPT) for Data Analysis 3

Chat Generative Pretrained Transformer (ChatGPT) for Data An...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Niranjanamurthy, M. Kantharaju, V. Satish, Sirisha Umashankar, Nidhi Raviram, V. BMS Institute of Technology and Management Dept. of AI&ML Bangalore India BMS Institute of Technology and Management Dept. of ISE Bangalore India Sri Siddhartha Institute of Technology Department of CS&E Tumakuru India

ISBN: (纸本)9798350317022

OpenAI created and published ChatGPT, an AI chatbot, in November 2022. It was developed using supervised and reinforcement learning methods and is based on OpenAI's GPT-3.5 and GPT -4 families of big language models. The creation of Artificial intelligence has started a new era in the field of computer science. Although it was first established in 1950, by, now known as the father of artificial intelligence, John McCarthy, and before that by Alan Turning who explored its mathematical possibilities, technology is generally ranked using a sigmoid curve, wherein you have a slow take off as the technology is invented, followed by an explosion of growth depending on how useful it is. That results in improvement in the technology as people find more real world practical applications for it, and then it reaches a stagnation point where you can't improvise the tech anymore. We believe that Artificial intelligence would be the start of a new sigmoid curve. Artificial intelligence has now reached a new peak after the maturation of ChatGPT. Currently, AI has a gradual growth rate of 21 % per annum, creating a new market in this highly competitive industry. This paper represented the ChatGPT features, paradigm and data analysis process. © 2023 IEEE.

关键词： ChatGPT Cons data analysis Pros Reinforcement Learning Stages of ChatGPT

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3rd international conference on Optimization, Learning Algorithms and applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, Learning Algorithms and applications, OL2A 2023

ISBN: (纸本)9783031530241

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