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International Conference on Intelligent Human-machine Systems and Cybernetics

作者： Qianjun Shuai Runze Wang Libiao Jin Long Pang School of information engineering Communication University of China Beijing China

The name has a rich connotation. In both English and Chinese, people's names usually have strong distinctions of gender. We can guess that strangers are male or female from their name. And the accuracy is high. This paper mainly makes the study of the gender recognition of Chinese names but it begins from the study of English gender recognition, step by step, looking for the rules, and use a variety of machine learning algorithms to search for a better model.

关键词： Gender recognition of names machine learning algorithm Classification Feature extraction

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Design and Application of machine learning algorithm Computer in Connect6 of Computer Games System 28

Design and Application of Machine Learning Algorithm Compute...

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28th Chinese Control and Decision Conference

作者： Wu, Gui Tao, Jun Jianghan Univ Educ Adm Off Wuhan 430056 Peoples R China Jianghan Univ Sch Math & Comp Sci Wuhan 430056 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA

ISBN: (纸本)9781467397148

The paper introduces briefly some of the key structures of the computer games system in connect6. And it mainly provides a further optimization for the current existing computer games system in connect6. The machine learning algorithm can increase the adaptive width adjustment in the MTD (f) algorithm and the self-learning method by TD_BP algorithm in the evaluation function which could strengthen the "thinking" ability on the computer games system in connect6. The advanced and modified algorithm is proved to be practical and applicative by experimentations and tests of computer games system in connect6 provided in this paper.

关键词： Connect6 machine learning algorithm Evaluation Function Computer Games

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A Novel Security algorithm RPBB31 for Securing the Social Media Analyzed Data using machine learning algorithms

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WIRELESS PERSONAL COMMUNICATIONS 2023年第1期131卷 581-608页

作者： Batcha, Bagath Basha Chan Singaravelu, Rajaprakash Ramachandran, Meenakumari Muthusamy, Suresh Panchal, Hitesh Thangaraj, Kokilavani Ravindaran, Ashokkumar Kommuri Pratap Reddy Inst Technol Dept Comp Sci & Engn Hyderabad Telangana India Vinayaka Missions Res Fdn Deemed Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor Tamil Nadu India Kongu Engn Coll Autonomous Dept Elect & Elect Engn Erode Tamil Nadu India Kongu Engn Coll Autonomous Dept Elect & Commun Engn Erode Tamil Nadu India Govt Engn Coll Dept Mech Engn Patan Gujarat India Sri Krishna Coll Engn & Technol Autonomous Dept Elect & Elect Engn Coimbatore Tamil Nadu India Bannari Amman Inst Technol Autonomous Dept Elect & Elect Engn Erode Tamil Nadu India

The present world data is most important in public because without data cannot live in the world. This data is big data and that data daily increases through Social Media like "Twitter, Facebook, Youtube, WhatsApp, Instagram, LinkedIn", etc.., because this media only share public opinion very fast through the net. From this media, more people used especially Twitter. Thus media are used to analyze the public opinion of the tweets and predict the future through machine learning algorithm. This analyzed data should make it polarity score. This score data has very little security because, this score can change the score and affected the future, so apply the existing security algorithms is Salsa and ChaCha. The Salsa algorithm is diagonal values moved to the first row. The ChaCha algorithm is diagonal values moved to the first column. The existing algorithms do not have good security because they focused only on performance, not security. So, the novel security algorithm is RPBB31. This algorithm has seven stages. The 1st stage is to find the secret key N, n, and p values from the matrix. The 2nd stage is to apply the secret key in P-N(n) operation. The 3rd stage operation of P-N(n) up to n = 1. The 4th stage is all P-N(n) operations make their single line. The 5th stage is to pair the values and swap the values in the matrix. The 6th stage is column operations in the matrix. The 7th stage is again Step 4 values used to swap but "0th" cell value start from reverse in the matrix. In world cricket is the most famous game. In all social media Millions of people are following in different manner, especially in indian cricketer are more famous in India and world. In some point of time it may lead to match fixing, betting. To overcome this kind of issue the four datasets chosen. The proposed algorithm has provide good security and performance while compare to existing algorithms.

关键词： Twitter machine learning algorithm Security Encryption RPBB31

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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 2015年第1期34卷 38-51页

作者： Ul Haque, Ashraf Mandal, Paras Meng, Julian Negnevitsky, Michael Montana State Univ Dept Elect & Comp Engn Bozeman MT 59717 USA Univ Texas El Paso Dept Ind Mfg & Syst Engn El Paso TX 79968 USA Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada Univ Tasmania Sch Engn Hobart Tas 7001 Australia

This paper presents a newstrategy for wind speed forecasting based on a hybrid machine learning algorithm, composed of a data filtering technique based onwavelet transform (WT) and a soft computing model based on the fuzzyARTMAP (FA) network. The prediction capability of the proposed hybrid WT + FA model is demonstrated by an extensive comparison with some other existing wind speed forecasting methods. The results show a significant improvement in forecasting error through the application of a proposed hybrid WT + FA model. The proposed wind speed forecasting strategy is applied to real data acquired from the North Cape wind farm located in PEI, Canada.

关键词： Fuzzy ARTMAP network machine learning algorithm short-term wind speed forecasting wavelet transform

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Design and Application of machine learning algorithm Computer in Connect6 of Computer Games System

Design and Application of Machine Learning Algorithm Compute...

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第28届中国控制与决策会议

作者： Gui Wu Jun Tao Educational Administration office Jianghan University School of Mathematics and Computer Science Jianghan University Department of Electrical & Computer Engineering Rowan University

ISBN: (纸本)9781467397155

The paper introduces briefly some of the key structures of the computer games system in *** it mainly provides a further optimization for the current existing computer games system in *** machine learning algorithm can increase the adaptive width adjustment in the MTD（f） algorithm and the self-learning method by TDP algorithm in the evaluation function which could strengthen the "thinking" ability on the computer games system in *** advanced and modified algorithm is proved to be practical and applicative by experimentations and tests of computer games system in connect6 provided in this paper.

关键词： Connect6 machine learning algorithm Evaluation Function Computer Games

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machine learning algorithms for building height estimations using ICESat-2/ATLAS and Airborne LiDAR data

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EARTH SCIENCE INFORMATICS 2024年第6期17卷 5123-5134页

作者： Agca, Muge Yucel, Aslihan Kaya, Efdal Daloglu, Ali Ihsan Kayalik, Mert Yetkin, Mevlut Yalcin, Femin Izmir Katip Celebi Univ Fac Engn & Architecture Dept Geomatics Engn Izmir Turkiye Iskenderun Tech Univ Dept Architecture & Urban Pla Dept Architecture & Urban Planning Antakya Turkiye PVI Elect Automat Ind Trade Ltd Sti Ankara Turkiye Izmir Katip Celebi Univ Fac Engn & Architecture Dept Engn Sci Izmir Turkiye

Building height information is essential for determining urban morphology, urban planning studies, and manage sustainable growth. This study aims to use machine learning algorithms to estimate building heights from airborne LiDAR and spaceborne ICESat-2/ATLAS data. The performance of different machine learning algorithms was investigated when analyzing ICESat-2/ATLAS and airborne LiDAR data. The accuracy of building height information was compared with field measurements. machine learning algorithms such as K-Nearest Neighbors (K-NN), Random Forest (RF), Support Vector machines (SVM), Artificial Neural Networks (ANNs), and Random Sample and Consensus (RANSAC) were used to classify spaceborne and airborne LiDAR data. Among all the algorithms applied to ICESat-2/ATLAS, the RF algorithm provided the best results for the strong and weak beams with 0.9683 and 0.9614, respectively. The K-NN yielded the best result for the airborne LiDAR dataset with 0.9999. Statistical analyzes were applied to both LiDAR datasets. The results of statistical analyzes for the pair of field measurement and ICESat-2 were R2 = 0.9894, RMSE = 0.4131, MSE = 0.1706, MAE = 0.3184, and ME = 0.0003;for the pair of field measurement and airborne LiDAR: R2 = 0.8368, RMSE = 1.9646, MSE = 3.8597, MAE = 1.0586, and ME = -0.3450;and for the pair of airborne LiDAR and ICESat-2: R2 = 0.8275, RMSE = 1.6664, MSE = 2.7770, MAE = 0.9040, and ME = 0.4598. As a result of the analysis, it was seen that the data obtained from the ICESat-2 system was successful in estimating building height and provided reliable data.

关键词： ICESat-2/ATLAS Airborne LiDAR machine learning algorithm Building Height Photon Laser

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An Efficient Unstructured Big Data Analysis Method for Enhancing Performance using machine learning algorithm ocr issue v22

An Efficient Unstructured Big Data Analysis Method for Enhan...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Reshmy, A. K. Paulraj, D. Anna Univ Madras 600025 Tamil Nadu India RMD Engn Coll Dept CSE Madras Tamil Nadu India

ISBN: (纸本)9781479970759

In this modern world, data mining technology holds an essential position in all the major Engineering fields. Handling of Unstructured Big Data is an essential task of this era. At present, making the maximum advantage of parallel processing know-hows and the task of rapid examination of huge data steadily and continuously transmitted or received from various sources is becoming popular or conventional. The big data analytics job is fragmented into smaller jobs and ran over tens, hundreds or thousands of product servers by the parallel processing architecture. This helps in maintaining the data center cost efficient and facilitates easy handling of the enormous work in an efficient way. In this paper, proposed solution takes online consumer purchase. The online system has unrivalled bank of data on online consumer purchasing behavior that can be mined from its 100 million customers accounts. They use customer click -stream data and historical purchase data of all those 100 million customers and each user is shown personalized results on customized web pages. For improving Big Data performance the machine learning Method i.e. K -Nearest Neighbour algorithm used to support to take good analysis. Hadoop simulator is used to solve this kind of task.

关键词： Unstructured Big Data machine learning algorithm Parallel Processing Technologies Hadoop Frame Work

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Simulation of thermal radiation image monitoring based on machine learning algorithms in logistics economic service development

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 57卷

作者： Zhang, Xiaohong Suzhou Polytech Inst Agr Dept Econ & Management Suzhou 215000 Jiangsu Peoples R China

With the rapid development of modern logistics industry, traditional monitoring methods are limited by environmental factors and equipment costs, thermal radiation image monitoring can provide more accurate and realtime monitoring data, the advantages are more obvious. A set of thermal radiation image monitoring system based on machine learning algorithm is developed to simulate the real-time monitoring and management of logistics economy services. In this paper, the real-time image data in the logistics process is collected by thermal radiation imaging equipment, and the collected thermal radiation images are preprocessed. The key features in the thermal radiation images are extracted by machine learning algorithm, and the classifier and detector models are constructed and trained. The trained model is integrated into a monitoring system, and a complete thermal radiation image monitoring solution is developed. The performance of the monitoring system is tested in a simulated logistics environment, and its effect in practical applications is evaluated. The results show that the system can accurately identify and track the key events in the logistics process, and meet the needs of real-time logistics monitoring. This technology can not only provide high-precision real-time monitoring data, but also effectively reduce the operating costs of logistics enterprises and improve logistics efficiency.

关键词： machine learning algorithm Heat radiation Image monitoring Logistics economic services Development simulation

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Applied Difference Techniques of machine learning algorithm and Web-based Management System for Sickle Cell Disease 8

Applied Difference Techniques of Machine learning Algorithm ...

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International Conference on Developments in eSystems Engineering (DeSE)

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiya Keenan, Russell Keight, Robert Fergus, Paul Idowu, Ibrahim Olatunji Liverpool John Moores Univ Sch Comp & Math Sci Appl Comp Res Grp Byrom St Liverpool L3 3AF Merseyside England Alder Hey Childrens Hosp Haematol Treatment Ctr Liverpool Paediat Haemophilia Ctr Liverpool L12 2AP Merseyside England

ISBN: (纸本)9781509018611

machine learning algorithm and web-based application systems have played a major role in improving the healthcare organisation in terms of continuous tele-monitoring therapy and maintaining telemedicine management systems. Currently, no intelligent system has been used in terms of managing sickle cell disease. However, this paper presents a system that facilitates a shift from manual methods to automated approach that can offer fast data collection with a reduced error rate. The system will be able to examine patient data and provide a suitable amount of Hydroxycarbamide drugs/liquid for each patient. By using a web-based system, we tend to improve patient welfare and mitigate patient illness before it gets worse over time, particularly with elderly people. The system will forward any critical concerns from the patient;it generates an automatic message to the medical doctors based on web-based platform in order to assist them with optimal decision-making. The initial case study that has been addressed in this project is how to make predictions for sickle cell disease for the amount of dose based on different architectures of machine learning in order to obtain high accuracy and performance. The most significant key for making predictions of the amount of medication is to enable healthcare organisation to provide accurate therapy recommendations based on previous data. The results using ANN showed that the proposed network produces significant improvements using the different evaluation methods. In our experiments, The MLP algorithm produced the best result in terms of the lowest error rates compare with other techniques. The Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error achieved 17887.55, 133.74, 90.20 and 0.1345, respectively.

关键词： Sickle Cell Disease machine learning algorithm Neural Network Web-based System Real-time data E-Health

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Advance Flood Detection and Notification System based on Sensor Technology and machine learning algorithm 22

Advance Flood Detection and Notification System based on Sen...

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22nd International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Khalaf, Mohammed Hussain, Abir Jaafar Al-Jumeily, Dhiva Fergus, Paul Idowu, Ibrahim Olatunji Liverpool John Moores Univ Appl Comp Res Grp Sch Comp & Math Sci Byrom St Liverpool L3 3AF Merseyside England

ISBN: (纸本)9781467383530

Floods are common natural disasters that cause severe devastation of any country. They are commonly caused by precipitation and runoff of rivers, particularly during periods of excessively high rainy season. Due to global warming issues and extreme environmental effects, flood has become a serious problem to the extent of bringing about negative impact to the mankind and infrastructure. To date, sensor network technology has been used in many areas including water level fluctuation. However, efficient flood monitoring and real time notification system still a crucial part because Information Technology enabled applications have not been employed in this sector in a broad way. This research presents a description of an alert generating system for flood detection with a focus on determining the current water level using sensors technology. The system then provides notification message about water level sensitivity via Global Communication and Mobile System modem to particular authorise person. Besides the Short Message Service, the system instantaneously uploads and broadcast information through web base public network. machine-learning algorithms were conducted to perform the classification process. Four experiments were carried out to classify flood data from normal and at risk condition in which 99.5% classification accuracy was achieved using Random Forest algorithm. Classification using Bagging, Decision Tree and HyperPipes algorithms achieved accuracy of 97.7 %, 94.6% and 89.8 %, respectively.

关键词： Flood detection system Sensor network Short message service Global communication and mobile system Real-time data machine learning algorithm

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