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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

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Research on Facial Expression Animation technology Based on K-means Algorithm 7th

Research on Facial Expression Animation Technology Based on ...

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7th international conference on Innovative computing, IC 2024

作者： Yan, Jiangting Pu, Pengju Ma, Dingding Department of Art and Design Shaanxi Fashion Engineering University Xi’an712046 China

ISBN: (纸本)9789819741205

Animation technology plays an important role in intelligent face expressions, but there is a problem of inaccurate capture and positioning. the traditional particle swarm algorithm cannot solve the animation technical problem in intelligent face expressions, and the effect is not ideal. therefore, this paper proposes research on facial expression animation technology based on K-means algorithm and analyzes the research on facial expression animation technology. Firstly, the distance measurement theory is used to locate the influencing factors, and the indicators is divided according to the requirements of animation technology to reduce the interference factors in animation technology. then, the distance measurement theory is used to form the K-means algorithm energy meter animation technical scheme, and the animation technical results is comprehensively analyzed. the MATLAB simulation results show that under certain evaluation criteria, the K-means algorithm is better than the traditional particle swarm algorithm in terms of animation technology accuracy and animation technology influencing factor time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： MATLAB

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Object Recognition based on Deep Learning Algorithms using Embedded IoT with Interactive Interface 7

Object Recognition based on Deep Learning Algorithms using E...

引用

7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Borde, Swapna Patil, Chandan Sonawane, Chinmay Singh, Mankrit Vidyavardhini's College of Engineering and Technology Vasai India B.E. Computer Engineering Vidyavardhini's College of Engineering and Technology Vasai India

ISBN: (纸本)9798350397253

Object detection is one of the most popular applications of machine learning in the modern era. With the growth of IOT in recent times, dedicated devices offering real-time object detection have seen overwhelming demand and applications in many sectors e.g. security, healthcare, workplace etc. Different algorithms and approaches have been implemented and studied in terms of object detection. To refer to a few the YOLO family, RCNN family, SSD etc. this research study compares the performance of YOLO and Faster RCNN based on a custom dataset containing different objects and items. the IOU of each data point (image) is calculated and compared. YOLO performs better for a small margin. In this research study, a language learning interactive model is demonstrated based on object detection(YOLO), NLP, python, flask and IoT (Raspberry Pi.) the web application is running on is divided into two parts, learning and practice. the learning part has a raspberry pi device which has a camera module that captures real-time footage, recognizes the object and reads out its name in the language the user is learning. the practice has the same setup, with the application asking the user to show a particular object. Points are awarded for correct guesses, making the learning process more interactive and involving. © 2023 IEEE.

关键词： Object detection

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. this paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. this research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. the GLRLM and GA features are provided independently to the ML models for training and testing. the ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). the metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. the combination of GA features with ANN produces the best results when compared to other combinations. the GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using Machine Learning 7

Bullyproof Sentinel: Fortifying Online Spaces Against Bullyi...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Dilshani, M.M.T. Vinoshan, T. Wijesinghe, W.A.S.D. Jathushan, V. Senarathne, Amila Gunasinghe, Amali Sri Lanka Institute of Information Technology Faculty of Computing Cyber Security Specialization Malabe Sri Lanka Sliit Cisco Academy Sri Lanka Institute of Information Technology Industry Engagement Unit Department of Computer Systems Engineering Manager Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341485

In the current social media landscape, cyberbullying and online harassment remain pressing issues. therefore, we planned a cyberbullying detection system for social media platforms. It can protect online communities from toxic behaviors and make a global impact by fostering a safer and more constructive online environment. We are mainly focused on developing an improved technique to detect bot profiles on social media, developing a mechanism to detect abusive content and sarcasm, developing a mechanism to detect defamatory comments on social media, and developing an effective method for detecting offensive and abusive content in image-format reviews on social media using machine learning algorithms. © 2023 IEEE.

关键词： Machine learning

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Small Signal Analysis of Photovoltaic based Water-Pumping System Driven by Induction Motor 7

Small Signal Analysis of Photovoltaic based Water-Pumping Sy...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Vannurappa, D. Pandu Ranga Reddy, G. Deepak, Karanam Hazarathaiah, Y. Sanakara Prasad, S. Malla, Siva Ganesh Department of Electrical & Electronics Engineering G Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool India CPGC Pvt. Ltd Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9781665464086

Photovoltaic (PV) operated water pumping systems driven by induction motors are becoming popular in many places. the system consists of without using any storage device can make the system less expensive. However, proper energy management through an effective control technique is required to managing power balance in the system. In order to analyze the system, a small signal analysis is required to achieve the better parameters of controllers used in the system. the nonlinear model of the PV based water pumping system driven by induction motor with the help of vector controller is transferred to linear model to analyze the dynamic behavior of the system. In order to control the PV terminal voltage and speed of the motor, two PI controllers are used and designing of the parameters of the controllers is discussed in this paper through the transfer function obtained from the linear model. the response of small signal function is presented in this paper with the help of MATLAB under changing of reference dc-link voltage. Apart from this, complete mathematical analysis is included step by step for better analysis purpose. © 2023 IEEE.

关键词： Controllers

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Comparative Study of Ransomwares 7

Comparative Study of Ransomwares

引用

7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on Big Data and Computational Intelligence, ICBDCI 2022

作者： Anand, Vivek Kumar Bamanjogi, Karunesh Shaw, Aryan Raj Faheem, Mahak NIIT University Computer Science and Engineering Neemrana India

ISBN: (纸本)9798350309904

Over the past few years cyberattacks have increased significantly as we have moved to a digital world. In 2021 this number rises drastically. Ransomware Attacks are not only limited to attacking an organization or a group of people to steal/encrypt their data, but it also refers to attacking networks or servers to slow down the services to such an extent thus creating the services unusable for the user, but at the same time, unlike other malware whose detection and analysis is not much complex, in ransomware, we find that they cannot be reverse engineered easily, due to high obfuscation and packing techniques involved. Skilled attackers are always a step ahead thus they find ways to evade the security systems or execute zero-day vulnerabilities. © 2022 IEEE.

关键词： Malware

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House Price Prediction using Machine Learning Algorithm 7

House Price Prediction using Machine Learning Algorithm

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sharma, Shailendra Arora, Deepti Shankar, Gori Sharma, Priyanka Motwani, Vihaan Department of ECE Shree Digambar Institute of Technology Rajasthan Dausa India Department of Mathematics Arya Institute of Engineering & Technology Rajasthan Jaipur India Department of ECE Jaipur Engineering College Rajasthan Jaipur India Department of Mathematics Jaipur Engineering College Rajasthan Jaipur India Shiv Nadar School Gurgaon India

ISBN: (纸本)9781665464086

Houses are one of the fundamental needs in human society. Good and peaceful surroundings where people feel comfortable are called houses. Hence, to have a good and happy living, people need to choose a good house model. this article focuses on accurately predicting house prices by using machine learning algorithms. this model helps people in selecting the house that is suitable for their living. the main parameter people look for will be the surrounding area, house type, price, location, and some other amenities. All these factors are consideredto find out the required house for living. As people are very concerned about their budgets for buying a house, the prediction of house prices should be very precise. It also helps people in choosing houses based on their budgets, which do not affect their financial state in the future. the main outcome of this model is to predict the price of a house accurately as per the user requirements. this study has attempted to implement various machine learning algorithms like Linear Regression (LR), Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random Forest (RF) Regressor algorithms. Finally, the algorithm that generates high accuracy is considered for predicting the house price. © 2023 IEEE.

关键词： Houses

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Finger Gesture Recognition using Recurrent Neural Network and Convolution Neural Network 7

Finger Gesture Recognition using Recurrent Neural Network an...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Kizhekethottam, Priyanka Sherekar, Amisha thakur, Pradnya Takale, Bhagyashree Pathan, Mehzabin Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Communication is essential for people to interact with one another. Sadly, not everyone can communicate verbally, so they resort to Sign language to engage in conversations and express themselves using various hand gestures. the deaf community mostly uses American sign language (ASL). However, only a few people can interpret ASL, and various translation systems have been developed. We aim to implement various Deep Learning techniques to translate ASL and read the sign out loud to find and develop an efficient and accurate sign language translation to make communication more accessible for hard-of-hearing individuals by building Recurrent Neural Networks and Convolution Neural Networks. On evaluating both these models highlighting the strengths and weaknesses of each approach. this is conducted based on various hyperparameters such as the Number of Epochs, Accuracy, Time, and Space Complexity. the study's findings offer exciting prospects for improving hand gesture recognition technology. © 2023 IEEE.

关键词： Accuracy American Sign Language Computer Vision Convolutional Neural Networks Efficiency Hand Gesture Recognition Image Analysis LSTM Mediapipe Performance Evaluation Recurrent Neural Networks Sign Language Translation Tensorflow Time Module

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A Survey: Machine Learning and Deep Learning in Wheat Disease Detection and Classification 7

A Survey: Machine Learning and Deep Learning in Wheat Diseas...

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7th international conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sahu, Nitin Bhat, Aruna Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350397253

Cereals are a significant and vital source of food for humans. To feed the world's expanding population, farmers must produce more crops. Plant diseases, however, have an impact on crop production and food quality. Wheat is among the most important crops consumed worldwide. Since Wheat Disease causes production loss, early disease detection and classification are absolutely essential. this research study has conducted a literature survey on studies published from the year 2017 to 2022 and summarized the three main types of Wheat Disease (fungal, bacterial, and insects), Wheat Disease datasets, and the current state of the art from the last six years of research. this research study has examined 24 studies on disease identification and classification by using various Machine Learning (ML) and Deep Learning (DL) algorithms. the proposed research analysis shows that the majority of the literature on Wheat Disease focused on fungal disease, and the majority of the datasets used were self-acquired. Many State-of-the-art models have already produced excellent results, and many more need to be developed. © 2023 IEEE.

关键词： Deep learning

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A High-Precision DQDS Algorithm 23

A High-Precision DQDS Algorithm

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Chuanying Xiao, Xiong Du, Peibing Jiang, Hao Barrio, Roberto Quan, Zhe Li, Kenli College of Information Science and Engineering Hunan University Changsha China Northwest Institute of Nuclear Technology Xi'an China College of Computer National University of Defense Technology Changsha China University of Zaragoza Departamento de Matemática Aplicada and Iuma Zaragoza Spain

ISBN: (纸本)9781665494571

With the rise of high-performance computing, the precision requirements for numerical calculations are getting higher and higher. Rounding errors cannot be avoided in floating point operations, and therefore the calculation result can be inaccurate and even incorrect when rounding errors are not taken into account. We study and analyze in detail the Differential Quotient Difference with Shifts (DQDS) algorithm, used in algorithms to calculate the eigenvalues of tridiagonal matrices, and propose a novel High Precision DQDS algorithm (HDQDS) based on Error Free Transformations (EFT). We use EFT to reduce the accumulation of errors and obtain a much more precise algorithm. the relative error of the HDQDS algorithm remains constant at the machine precision error level, while the relative error of the DQDS algorithm has fluctuations in the same experimental scenario. Numerical experiments show that the proposed high-precision HDQDS algorithm allows for higher precision and more stability than the original alagrithm. © 2021 IEEE.

关键词： Fluctuations Scientific computing Stability analysis Eigenvalues and eigenfunctions Numerical stability

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