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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Kexin Chen International School Beijing University of Posts and Telecommunications Beijing China

Artificial Intelligence technologies have been developed rapidly in the past 20 years and have been widely applied in various industries. Machine Learning, as the most important way to achieve artificial intelligence, is evolving at a huge speed. Keeping track of the regular development is quite a difficult work, especially for beginners. In this paper, we are going to briefly discuss about the most popular machine learning algorithms and their variants or improved versions. these algorithms are usually classified as supervised learning or unsupervised learning methods, or other grouping methods based on functionality. Deep Learning is covered because deep learning methods have led to revolutionary progress in computer vision and machine learning. We also talk about the industries that these models can be applied in. this article is intend to provide basic references of these commonly used algorithms in the field for beginners, help them to get a general understanding of the algorithms and the scenarios, and guide to select appropriate algorithm or model to start solving a practical data problem.

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Maximum Likelihood Decoder For Image Watermarking Techniques 6

Maximum Likelihood Decoder For Image Watermarking Techniques

引用

6th IEEE international conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Sharma, Preeti Chitkara University Institute Of Engineering And Technology Punjab India

ISBN: (纸本)9781665425520

the paper extends a general approach for extracting the watermark using a mathematical technique for watermarking using discrete Wavelet Transform (DWT). though the watermark may be inserted using quantization or a combination of other transforms, yet the successful recovery of watermarks is a difficult task. It is indeed a challenge for the decoder to recover the watermark when various attacks are applied upon a watermarked image. the corresponding scheme of watermark decoding uses the principle of Maximum Likelihood (ML) estimation and is based upon the statistical modeling of parameters of Gaussian distribution. the watermarking experiments utilizing ML decoder exhibit improved performances over the conventional approaches of watermarking employing correlation detectors. the robustness of such techniques is verified through the values of Bit Error Rate (%). © 2021 IEEE.

关键词： discrete wavelet transforms

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Air Quality Monitoring and Forecasting System using IoT and Machine Learning Techniques 6

Air Quality Monitoring and Forecasting System using IoT and ...

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6th international conference on Green Technology and Sustainable Development, GTSD 2022

作者： Tran, Quynh Anh Dang, Quang Hung Le, Tung Nguyen, Huy Tien Le, Tan Duy School of Computer Science and Engineering International University Ho Chi Minh City Viet Nam Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (数字)9781665466288

ISBN: (纸本)9781665466288

Air pollution has been a growing concern in the twenty-first century, affecting the surrounding environment and public health. the previous studies have recently undertaken significant research on air pollution and air quality monitoring. Unfortunately, this area continues to be challenged by unresolved issues. this paper proposes an IoT-based Air Quality Monitoring and Forecasting System to monitor and predict air pollution for a specific area based on various pollution factors. Using Arduino UNO R3 and various low-cost sensors, our IoT system can collect and monitor pollutants, such as PM2.5, CO2, CO, as well as temperature and humidity. the air quality data was collected for several months. To overcome the problems of instability of low-cost devices in monitoring, machine learning (ML) algorithms, such as K-Nearest-Neighbour (KNN), Expectation-Maximization (EM), Multiple Imputation by Chained Equations (MICE), and Autoregressive-Moving-Average (ARMA), are applied to address missing data and outliers due to technical issues. the KNN model outperformed all others in terms of RMSE, MSE, MAE, R-squared, and execution time. then, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) algorithms are applied to predict future air quality. the result shows that our system can predict the air quality factors over the next hour with the highest accuracy at 96 %. Finally, a web interface was created to monitor and forecast air quality in real-time. © 2022 IEEE.

关键词： Long short-term memory

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An Improved HVC based blind watermarking algorithm using SVD and DWT

An Improved HVC based blind watermarking algorithm using SVD...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Abhishek Toofani Hitendra Garg Department of Computer Engineering & Applications GLA University Mathura India

Different watermarking techniques are widely adapted to protect the digital content that is available on the Internet from piracy and infringement. Most of these algorithms for marking are not effectively attack-proof and robust. this paper presents an optimized watermarking algorithm for scale factors using human visual characteristics with singular value decomposition. the action follows SVD and Wavelet transform for breaking the image into block vectors. As a result, the embedding coefficient is determined to balance the watermark image value and extracted value. the derived methodology works on scaling factors, providing good results against noise and attacks.

关键词： Visualization Image coding Watermarking Color discrete wavelet transforms Internet Cryptography

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Using Advanced Surface Movement Guidance and Control System for Kaohsiung Xiaogang Airport Aircraft's Route Assignment Planning Based on Petri nets 8

Using Advanced Surface Movement Guidance and Control System ...

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8th international conference on applied System Innovation, ICASI 2022

作者： Pan, Yen-Liang Jhang, Jia-Cing Gu, Fu-Rong Mathematics and Physics Division General Eduation Center Air Force Academy. No. Sisou 1 Jieshou W. Rd. Gangshan Dist. Kaohsiung City Taiwan 6th Composite Wing of ROCAF. No. Shengli 330 Rd. Pingtung Country Pingtung City Taiwan

ISBN: (纸本)9781665496506

Advanced Surface Movement Guidance and Control System (A-SMGCS) has been widely used in major civil aviation airports worldwide to perform aircraft ground assignment path planning and deployment. therefore, this study uses the Petri Nets theory combined with the Advanced Surface Dynamic Guidance Control System (A-SMGCS) to design the aircraft assignment path planning and aircraft dynamic simulation at Kaohsiung Xiaogang Airport. And through the well-known simulation software HP Sim to verify its design effect. Preliminary research results show that this design can indeed complete the planning and deployment of aircraft ground assignment paths under the current capacity of Kaohsiung Xiaogang Airport. However, in the case of an increase in the number of aircraft, it may be limited, but it can verify the potential conflict problems in the airport scene dynamics. In addition to designing a suitable controller, this paper also hoped that it could effectively detect the potential conflict of the scene dynamics, reduce the accident rate and increase the airport flight capacity. In the future, the results of this research can be adapted as a reference in aviation safety applications. © 2022 IEEE.

关键词： Petri nets

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Entrepreneurship in the Automotive Industry Using Computational Modeling Technology 4

Entrepreneurship in the Automotive Industry Using Computatio...

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4th international conference on applied mathematics, Modeling and Computer Simulation, AMMCS 2024

作者： Ali, Amr Salah Hamed Ramadan Ali, Akram M.S. Ali, Salah H.R. Malta Giza Egypt at 6th of October Giza Egypt

ISBN: (纸本)9781643685519

Nowadays, entrepreneurship plays a fundamental role in managing successful projects. therefore, entrepreneurship has become the focus of any new progress in the technology of the industry-based economy. Computational modeling technology as one of the information technology also fulfils a direct role in predicting the optimal design in engineering industries to avoid any effects that may be exposed to engineering parts or systems. Modern technologies have become using advanced simulation method to achieve entrepreneurial work for sustainable development in analyzing the influential parameters in engineering industries. In this work, the concept and importance of entrepreneurship in automotive industries are highlighted, especially in automotive engines. Along with studying and analyzing the influential factors as a case study of a car engine as a complex engineering system using computing modeling technology. the development of computing programs and their speed revealed an accurate description of the tolerance of engineering parts along with reducing design and manufacturing costs. this paper summarizes the importance of the role of advanced simulation technology systems in maximizing the natural and acquired potential of functional materials as an influential factor in being truly entrepreneurial. Simulation experiments have proven the possibility of reducing the impact of stresses in exchange for the appropriate selection of some proposed engineering materials that suit the best car engine. Titanium has been proven to be the best material for reducing deformation and stresses on the engine with a high safety factor compared to other materials. Eventually, the use of computational modeling technology has proven to play a decisive pioneering role in developing this interesting field to provide solutions to important issues in the future of mechanical design systems to improve production quality. © 2024 the Authors.

关键词： Automotive industry

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An Undersampled Model for Automated Sleep Stage Scoring Using EEG Data: Utilization of DWT, bagged trees, and random undersampling to achieve more consistent accuracy on the sleepstage problem 22

An Undersampled Model for Automated Sleep Stage Scoring Usin...

引用

6th international conference on Deep Learning Technologies, ICDLT 2022

作者： Li, Zachary Yang, James Liu, Jianguo Texas Academy of Mathematics and Science University of North Texas DentonTX76203 United States

ISBN: (纸本)9781450396936

Sleep is one of the most critical functions of the human body, yet many disorders disrupt this physiological process. these conditions can be diagnosed by observing the pattern and length of sleep stages that a patient enters;however, this process requires the manual scoring of a patient's EEG patterns by a specialist. this process is time-consuming and inaccessible, but the accurate and automated scoring of sleep stages by artificial intelligence would help medical professionals quickly offer diagnoses and treatments. In this paper, we propose a bagged trees model using wavelet decomposition for feature extraction, while also utilizing random undersampling to handle the inherent data imbalance. We achieve 85.1% and 87.1 % accuracy on 5-fold cross validation and the test set, respectively. the accuracy across all stages is consistent, indicating that the model may be more suitable for real-world applications than other models with nominally higher accuracies. © 2022 ACM.

关键词： discrete wavelet transforms

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YJWS2023: High-resolution optical remote sensing image dataset of typical military ship target 6

YJWS2023: High-resolution optical remote sensing image datas...

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6th conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

作者： Ma, Lan Wang, Xiao Du, Lin College of International studies NUDT China

ISBN: (数字)9781510679733

ISBN: (纸本)9781510679726

At present, deep learning and other artificial intelligence technologies are constantly applied in the field of remote sensing, and deep learning needs massive data for model driving. However, the development of ship datasets based on optical remote sensing images is slow and not very professional. To solve this problem, we propose three stages of ship intelligence interpretation, and construct a new ship data set for the development of the first stage. the data set reclassifies the existing ships, combined with the purpose of ship target identification and the current development direction of military ships, and is divided into six types of labels, including aircraft carriers, escort ships, submarines, dock landing ships, supply ships and other auxiliary ships. the 909 samples were manually annotated, with the training set contains 795 samples and the validation set contains 114 samples. Compared with the commonly used optical remote sensing ship data sets in China, the data set we proposed has following properties: 1. Professional. the data set targets military ships, the data sources from all ships in the world's military ports;2. Systemic. three stages of automatic identification are proposed;3. Classification and rationality. the classification of this data set combines the general ship classification criterias, practical identification needs and the developments of the ship itself. the world's military ships are divided into six categories;4. Uneven nature. the number of sliced samples of each type of ships is unbalanced;5. Developability. this data set can consistently increase the number of samples. New classes of ships will emerge in the short terms. the class can also be divided into six categories according to their missions;6. the openness. All data for this data set were obtained from Internet public data. through the verification and evaluation of this data set using five target detection algorithms of YOLO series, the accuracy of the target detection

关键词： Optical remote sensing

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Data Mining Approach in Predicting House Price for Automated Property Appraiser Systems 6th

Data Mining Approach in Predicting House Price for Automated...

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Yousir, Naeem th. Abdulameer, Shaymaa Mohammed Mostafa, Salama A. College of Information Engineering Al-Nahrain University Baghdad10072 Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

ISBN: (纸本)9789819930098

the house price prediction model can be a logistic tool in assisting individuals and companies to determine the cost of a property or a house on sale and the best time to acquire a house. With the ever-increasing in house price rates, data mining approaches have been used as a property appraiser system to predict a house price based on house features before making a deal. Existing research in house prediction involves collecting a number of house price estimation attributes into a dataset of house price features. this study is aimed at building a model for predicting house price based on the house price features dataset. then dataset is run through machine learning (ML) algorithms to learn the pattern and predict new house price cases. the cross-industry standard process for data mining (CRISP-DM) method is used for this research. Decision tree regression (DTR), linear regression (LR), multiple linear regression (MLR), and random forest regression (RFR) are the ML algorithms applied in this research. the results have shown that RFR is the most suitable algorithm for this dataset, with an average coefficient of determination (R square) of 82.09%, followed by the DTR with an R square score of 73.32% when using different training and learning splitting ratios. An application of the RFR model can be used in real estate companies for estimating the prices of properties. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiple linear regression

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Securing Multimedia Content Using Watermark and Digital Signature 6

Securing Multimedia Content Using Watermark and Digital Sign...

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6th IEEE international conference on Communication and Electronics Systems, ICCES 2021

作者： Rana, Md. Sohel Akther, Md. Shoaib Rahman, Md. Mahbubur Dept. of Ict Dhaka Bangladesh Dept. of Eee Saidpur Bangladesh Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9781665435871

With the increasing rate of multimedia usages, many processes have been introduced to ensure the safety of their contents. Digital signature and watermarking are the processes that are used in cryptography and digital image transmission respectively. these two techniques can be combined for secure multimedia transmission. Over the years, these technologies have been able to secure the data of text documents but securing multimedia contents have been a difficult task. therefore, this research proposed to use the digital signature and watermarking to secure multimedia data. the multimedia data is embedded with another data and is transmitted after encrypting it using the cryptographic algorithms. the receiver end decrypts the data and extracts the original data from the watermark. For digital watermarking, various transformations have been suggested. In this exploration, discrete Wavelet Transform (DWT) has been used as it shows better results in digital watermarking applications. © 2021 IEEE.

关键词： discrete wavelet transforms

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