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Intelligent Systems Conference

作者： Ampofo, Isaac Atta Senior Abdulai, Mobarik Karikari, Maxwell Twumasi, Lydia Ampofo, Isaac Atta Junior Ampofo, Beatrice Univ Liverpool Brownlow Hill Liverpool L69 7ZX Merseyside England Univ Educ Winneba Ghana Kwame Nkrumah Univ Sci & Technol Kumasi Ghana Aketen Appaih Menka Univ Skills Training & Entrep 1277 IPT Rd Kumasi Ghana

ISBN: (纸本)9783031664274;9783031664281

The incidence of cancer is rising dramatically on a global scale. Many people receive a late-stage diagnosis despite though cancer is preventable and well-treated in its early stages. Furthermore, cancer frequently returns following protracted treatment. Therefore, it is essential to anticipate cancer recurrence to proactively pursue specific treatments. The study review literature done on machine learning algorithm for cancer prediction using bibliometric review and Vosviewer. The studies that were taken into consideration for this research were obtained from the Web of Science database. A range of search terms, such as pertinent keywords (machine learning, cancer detection, cancer prediction), were utilized to examine the title, keywords, and abstract of articles inside the database covering the years 2014 through 2023. 1914 documents were used for the study. Articles published in IEEE Access have a greater impact, but Scientific Reports had received more citations than articles in other journals-roughly 1018 of all the citations for the chosen documents come from articles published in this journal. Research on International evaluation of an AI system for screening of breast cancer had the highest citations 1059. India has the highest documents of 430 representing 22.419%. For citations, the USA has the highest of 122,072. Egyptian Knowledge Bank EKB has the highest documents of 61 representing 3.180%. National Natural Science Foundation of China (NSFC) has the highest documents of 131 representing 6.830%. machine learning must be incorporated into clinical practice and trials due to the changing nature of cancer and how it is treated over time.

关键词： Cancer Bibliometric review Prediction VoS viewer machine learning algorithm

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machine learning algorithm for Student's Performance Prediction 10

Machine Learning Algorithm for Student's Performance Predict...

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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Hasan, H. M. Rafi Islam, Mohammad Touhidul Rabby, A. K. M. Shahariar Azad Hossain, Syed Akhter Daffodil Int Univ Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781538659069

Student performance prediction is very important to understand a student progress rate. It is said that 'Prevention is better than the cure'. In this Research, we are trying to find out student's current status and predict his/her future results. After the outcome, teachers can give him/her proper advice to avoid the poor result and also can groom the student. By finding out the dependencies for final examinations. Which courses he/she should take in the upcoming semester (roles of adviser/teacher). Every year a lot of students lag behind because of lack of proper advice and monitoring. A teacher can't monitor each and every single student at once. If a system can help a Teacher about the students like which student needs which kind of help. Then it will be much helpful for both teachers and student. The aim is helping the student to avoid his/her predicted poor result using Artificial Intelligence. If a student could know what will be his/her result in the future and notify him/her what to do to avoid his/her the bad results by predicting the final examinations mark. This research would be helpful for the students and teachers with The highest accuracy of 94.88%.

关键词： Student Performance Prediction machine learning algorithm Academic result k-nearest neighbor

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Identification of Mild Cognitive Impairment by machine learning algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第36期35卷 25121-25130页

作者： Li, Haozhen Univ Southern Calif Leonard Davis Sch Gerontol Los Angeles CA 90089 USA

With the aging of the population, China has a large population base. The number of people suffering from mild cognitive impairment has gradually increased and gradually turned to senile dementia. The probability of mild cognitive impairment in China is about 5-7%, and about 15 million people are ill. To better distinguish mild cognitive impairment from Alzheimer's, this paper adopted the machine learning (ML) method to model. The evaluation table was established by considering the reaction time, education, background, memory, and other aspects of the patient. machine learning has been applied in the fields of the Internet, finance, medicine, automation, biological science, etc. Through the machine learning support vector machine (SVM) and linear regression, the classification accuracy was compared. The results showed that the classification accuracy of SVM was 87.92% under the ML algorithm. It also showed that ML was more conducive to classification and recognition, which has played an important role in the identification of mild cognitive impairment in the current aging population.

关键词： Mild cognitive improvement machine learning algorithm Population aging Support vector machines

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Research on state evaluation and risk assessment for relay protection system based on machine learning algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第18期14卷 3619-3629页

作者： Ying, Liming Jia, Yongtian Li, Wenan Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

The relay protection system plays an important role in ensuring the stable operation of power systems. Combined with operation data collected from a region in China, this study is aimed at providing a reliable quantitative basis for relay protection systems' operating maintenance by the aid of a semi-supervised Mahalanobis distance machine learning algorithm. The evaluation result is first applied as a training set on the basis of the analytic hierarchy process fuzzy synthetic evaluation. Then, contrastive analysis is conducted in terms of accuracy, processing time, and feasibility. It includes comparative cases with a supervised multiple regression analysis algorithm and unsupervised K-means algorithm. The comparison result reveals that the algorithm can effectively and accurately predict the running state of the equipment and offer a quantitative reference for relative maintenance strategy.

关键词： learning (artificial intelligence) relay protection fuzzy set theory regression analysis power engineering computing power system protection reliable quantitative basis relay protection systems Mahalanobis distance machine analytic hierarchy process fuzzy synthetic evaluation supervised multiple regression analysis algorithm unsupervised K-means algorithm state evaluation risk assessment relay protection system machine learning algorithm stable operation power systems operation data

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Modeling Alpine Grassland Above Ground Biomass Based on Remote Sensing Data and machine learning algorithm: A Case Study in East of the Tibetan Plateau, China

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 2986-2995页

作者： Meng, Baoping Liang, Tiangang Yi, Shuhua Yin, Jianpeng Cui, Xia Ge, Jing Hou, Mengjing Lv, Yanyan Sun, Yi Nantong Univ Inst Fragile Ecoenvironm Sch Geog Sci Nantong Peoples R China Lanzhou Univ Coll Pastoral Agr Sci & Technol State Key Lab Grassland Agroecosyst Lanzhou 730000 Peoples R China Lanzhou Univ Coll Earth & Environm Sci Minist Educ Key Lab Western Chinas Environm Syst Lanzhou 730000 Peoples R China

Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that 1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R(2) 0.04 & x2013;0.39, and RMSE 859.68 & x2013;1075.09 & x00A0;kg & x002F;ha, respectively;2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate nonparameter models;3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate & x002F;multivariate parameter models, RMSE of RF model decreased 26.45 & x0025;& x2013;44.27 & x0025;. Meanwhile, RF models can explain 89.41 & x0025;variation in AGB during grass growing season. This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy based on MODIS data was greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

关键词： Biological system modeling MODIS Estimation Vegetation mapping Soil Biomass Remote sensing Above ground biomass (AGB) alpine grassland machine learning algorithm multivariate model

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Sentimental analysis over twitter data using clustering based machine learning algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-12页

作者： Jacob, Sharon Susan Vijayakumar, R. Mahatma Gandhi Univ Sch Comp Sci Kottayam Kerala India

Using Internet technologies, people often share their thoughts, feedback, news and information with others. Owing to social media sites, speed and ease of contact improved. User posts their views on public sentiment web sites like Facebook, Instagram, Twitter, blogs, WhatsApp, Snapchat, LinkedIn, etc. Thousands of posts, millions of tweets and thousands of letters are posted each day. Twitter is one of them that is now being widely popular in these social media sites. It offers an easy and quick way to evaluate the opinions of consumers on a product or service. The approach to be used to mark customer's impressions or opinions of a product is to establish an sentimental analysis system. Twitter is a microblogging platform where users can submit feedback to a community of followers in the form of ratings or tweets in bigdata. A tweet may be defined as positive, negative or neutral depending on the viewpoint shared. Here we investigate the sentiment of Twitter messages in this paper using the clustering approach based on machine learning (ML) algorithm. Our tests are carried out in a qualified and test collection composed of large data from tweets from one lakh of results, showing our work to determine if a tweet is positive or negative.

关键词： Twitter Sentiment analysis Clustering Big data machine learning algorithm

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Preparation process and performance of polyurethane modified bitumen investigated using machine learning algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第7期56卷 6775-6800页

作者： Lu, Pengzhen Huang, Simin Zhou, Chenhao Xu, Zijie Wu, Ying Zhejiang Univ Technol Hangzhou 310014 Zhejiang Peoples R China Jiaxing Nanhu Univ Jiaxing 314001 Zhejiang Peoples R China

In the preparation of high-performance polyurethane (PU) modified bitumen, due to the different kinds of PU modifiers, the design parameters of the preparation process are numerous, and indexes of the performance response need to be selected. As a result, the preparation process of PU-modified bitumen is not universally applicable. Therefore, according to different application environments, how determining the process parameters of the PU-modified bitumen accurately and efficiently is a key problem to be solved urgently. Based on fthe Kriging-PSO hybrid optimization algorithm, this paper proposed a novel design method for the preparation process for the PU-modified bitumen. The response indicators with high relative sensitivity (softening point, rutting factor, Brookfield viscosity, and dispersion coefficient) were screened by using range and variance analysis to improve the fitting accuracy of the Kriging-PSO model after training. Among them, the dispersion coefficient was evaluated by fluorescence microscopy test using the Christiansen coefficient method to evaluate the uniformity of the dispersed phase of the PU modifier. Through the Kriging-PSO algorithm, the main process parameters for preparing PU-modified bitumen in the laboratory were determined as follows: shearing time 86 min, shearing speed 2450 rpm, shearing temperature 148, and PU content 18.6%. The prepared PU-modified bitumen was placed in an oven at 100 for 2 h. The performance indicators of PU modified bitumen were: softening point 90, rutting factor 30 kPa, Brookfield viscosity 80,000 Pa center dot s, and dispersion coefficient 0.92. The PU-modified bitumen prepared by this optimal process met the expected performance indicators. The results of this paper showed that the Kriging-PSO algorithm provided a new idea for the design of a modified bitumen preparation process and achieve the purpose of designing the optimum process parameters of PU modified bitumen efficiently using fewer samples. Meanwhil

关键词： Polyurethane modified bitumen Preparation process machine learning algorithm Kriging-PSO model

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A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 144卷 325-340页

作者： Teluguntla, Pardhasaradhi Thenkabail, Prasad S. Oliphant, Adam Xiong, Jun Gumma, Murali Krishna Congalton, Russell G. Yadav, Kamini Huete, Alfredo US Geol Survey 2255 N Gemini Dr Flagstaff AZ 86001 USA BAERI NASA Res Pk Moffett Field CA 94035 USA Int Crops Res Inst Semi Arid Trop Hyderabad Telangana India Univ New Hampshire Dept Nat Resources & Environm 56 Coll Rd Durham NH 03824 USA Univ Technol Sydney POB 123 Broadway NSW Australia

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013-2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4-6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32-48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was

关键词： Cropland mapping Landsat machine learning algorithm Random forest Google Earth Engine Australia China

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Integrated method for intelligent structural design of steel frames based on optimization and machine learning algorithm

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ENGINEERING STRUCTURES 2023年第1期284卷

作者： Shan, Wenchen Liu, Jiepeng Zhou, Junwen Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518061 Peoples R China

Optimization methods using metaheuristic algorithms have been widely used in steel frame design to improve the inefficient traditional design method due to repeated model tuning and massive mechanical analysis. However, the random search feature of them may easily result in poor performances. In this paper, combining metaheuristic algorithms and machine learning methods, a highly integrated method based on an on-line model training, updating and parameter tuning process is proposed to improve the performance of the optimization algorithm with general forms and parameters. It reduces the impact of the iterative mechanism and parameter setting of metaheuristic algorithms on their performance. Such method is introduced to intelligent structural design of steel frames including three steps. The standard optimization process is conducted to search optimal design and simultaneously collect the mechanical analysis data of the structure. Then the data is adopted to generate and update surrogate models of structural responses dynamically while an analysis-based feature en-gineering and an automatic model tuning technique are employed to improve the model accuracy. Finally, a much more efficient procedure is presented to obtain potential solutions which are used to improve the convergence rate and performance of standard optimization. Four cases are used to study the effectiveness of the integrated method and the influence of different settings is discussed, as well as its generality. As a conclusion, the proposed method can achieve structural safety and economic benefit of steel frames, which exhibits supe-riorly in terms of robustness, optimal results and computational cost even in large-scale optimization problems of complicated frames.

关键词： Steel frame Metaheuristic algorithm machine learning algorithm Surrogate model Intelligent structural optimization design

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Inversion of Chlorophyll-a Concentration in Donghu Lake Based on machine learning algorithm

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WATER 2021年第9期13卷 1179-1179页

作者： Tang, Xiaodong Huang, Mutao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 mu g/L and 12.66 mu g/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 mu g/L and 16.49 mu g/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

关键词： chlorophyll-a inversion machine learning algorithm Donghu Lake

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