检索结果-内蒙古大学图书馆

A Tomato Fruit Biomass Prediction Model for Aquaponics System Using machine learning algorithms

IFAC-PapersOnLine 2022年第1期55卷 709-714页

作者： Pragnaleena Debroy Lalu Seban Department of Electronics & Instrumentation Engineering National Institute of Technology Silchar Assam India

Fruit biomass prediction is a foremost practice to ensure an enhanced production rate and profit maximization. This paper proposes two prediction models for tomato biomass estimation of the aquaponics system using the Artificial Neural Network (ANN) and its hybrid with fuzzy logic i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS). The Feed-forward back propagation network is used in the ANN model and in the ANFIS model, the Gaussian membership function is employed with Sugeno Fuzzy Inference System. The performance evaluation indicated that ANFIS model had attained the best prediction accuracy in terms of MAE of 0.1079 and RMSE of 0.4582, and R 2 of 0.9918 in comparison with the conventional ANN model. This prediction model can help predict the tomato fruit growth of the aquaponics system simply and cost-effectively, supporting farmers in avoiding imbalances in market supply and demand, and economic management.

关键词： Aquaponics System Tomato fruit Biomass Estimation machine learning algorithms Artificial Neural Network (ANN) Adaptive Neuro-Fuzzy Inference System (ANFIS)

来源：评论

学校读者我要写书评

暂无评论

Forecasting US movies box office performances in Turkey using machine learning algorithms

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 6579-6590页

作者： Cagliyora, Sandy Oztaysi, Briar Sezgin, Selime Kadir Has Univ Dept Business Adm Kadir Has Str TR-34083 Istanbul Turkey Istanbul Tech Univ Dept Ind Engn Macka Istanbul Turkey Bilgi Univ Dept Business Adm Istanbul Turkey

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

关键词： machine learning algorithms motion picture industry forecasting

来源：评论

学校读者我要写书评

暂无评论

A Survey of Voice Pathology Surveillance Systems Based on Internet of Things and machine learning algorithms

引用

IEEE ACCESS 2020年 8卷 64514-64533页

作者： Al-Dhief, Fahad Taha Latiff, Nurul Mu'azzah Abdul Abd Malik, Nik Noordini Nik Salim, Naseer Sabri Baki, Marina Mat Albadr, Musatafa Abbas Abbood Mohammed, Mazin Abed Univ Teknol Malaysia UTM Sch Elect Engn Fac Engn Johor Baharu 81310 Malaysia Sohar Univ Comp & Informat Technol Sohar 311 Oman Univ Kebangsaan Malaysia Fac Med Dept Otorhinolaryngol Med Ctr Kuala Lumpur 56000 Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi 43600 Malaysia Univ Anbar Coll Comp Sci & Informat Technol Ramadi 45654 Iraq

The incorporation of the cloud technology with the Internet of Things (IoT) is significant in order to obtain better performance for a seamless, continuous, and ubiquitous framework. IoT has many applications in the healthcare sector, one of these applications is voice pathology monitoring. Unfortunately, voice pathology has not gained much attention, where there is an urgent need in this area due to the shortage of research and diagnosis of lethal diseases. Most of the researchers are focusing on the voice pathology and their finding is only to differentiating either the voice is normal (healthy) or pathological voice, where there is a lack of the current studies for detecting a certain disease such as laryngeal cancer. In this paper, we present an extensive review of the state-of-the-art techniques and studies of IoT frameworks and machine learning algorithms used in the healthcare in general and in the voice pathology surveillance systems in particular. Furthermore, this paper also presents applications, challenges and key issues of both IoT and machine learning algorithms in the healthcare. Finally, this paper highlights some open issues of IoT in healthcare that warrant further research and investigation in order to present an easy, comfortable and effective diagnosis and treatment of disease for both patients and doctors.

关键词： Internet of Things machine learning algorithms the healthcare sector voice pathology surveillance systems

来源：评论

学校读者我要写书评

暂无评论

Fuzzy mathematics and machine learning algorithms application in educational quality evaluation model

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5583-5593页

作者： Wang, Jian Zhang, Weizhong Quzhou Coll Technol Quzhou Peoples R China Zhejiang Normal Univ Jinhua Zhejiang Peoples R China

Teaching quality evaluation is a complex non-linear system fitting problem under the influence of many factors. The establishment of teaching quality evaluation is to construct a functional relationship between teaching quality evaluation index and teaching effect. In this paper, the authors analyze the fuzzy mathematics and machine learning algorithms application in educational quality evaluation model. machine learning method has been well applied in complex problems such as classification, fitting, pattern recognition and so on. It can be used to realize a more comprehensive, reasonable and effective evaluation of the classroom teaching quality of university teachers. The simulation results show that the model can well express the complex relationship between the teaching quality evaluation index and the evaluation results. The theoretical values of the evaluation results are in the corresponding confidence interval, which proves that the machine learning algorithm has good reliability for different teaching quality evaluation problems.

关键词： Fuzzy mathematics precision contrast machine learning algorithms education quality

来源：评论

学校读者我要写书评

暂无评论

Attendance automatic recognition and learning behavior of web-based course attendance based on machine learning algorithms

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1769-1777页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou 213032 Jiangsu Peoples R China

At present, the automatic attendance mode of distance education is not conducive to the confirmation and analysis of information after class. In order to study the effective automatic recognition algorithm of remote education classroom, this study takes the educational classroom of intelligent innovation and entrepreneurship of Internet + as an example for analysis. Moreover, this paper adopts facial features as the basis of recognition, establishes corresponding positioning points, and constructs precise positioning methods for real-time feature capture. At the same time, the ASM algorithm is used to extract facial features, and the algorithm is improved to improve the extraction effect. In addition, this paper proposes Gabor-wavelet packet set and Gabor beamlet set for auxiliary recognition, which improves the recognition rate. Finally, this paper designs experiments to analyze the performance of the algorithm of this study. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for subsequent related research.

关键词： Feature recognition automatic identification personnel management machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Time-Series Data Classification and Analysis Associated With machine learning algorithms for Cognitive Perception and Phenomenon

引用

IEEE ACCESS 2020年 8卷 222417-222428页

作者： Jeong, Taikyeong Sehan Univ Dept Artificial Intelligence Software Engn & Sci Pochon 31746 South Korea

Analysis and collection of time-series data as a major role of machine learning has been emphasized with an important key in cognitive science. Because the cognitive mechanisms such as human sensation and perception from cognitive science are fast responses ranging from a few milliseconds to hundreds of milliseconds, the method of pattern recognition and analysis of these brain signals must be done and it is necessary to derive some information. In this paper, we investigated time-series data of cognitive function of the brain obtained using a non-invasive technique on multiple channels via signal classification and analysis, using a cognitive science approach and experiments. The test dataset was collected in 19 channels using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) techniques with multiple rests and working conditions on eight subjects. From this perspective, the main contributions of this paper are that it completes the collection and analysis of cognitive-scientific time-series data and has scientific implications that extend to other integrated domains, energy, manufacturing, bioinformatics, and finance area. The use of Shapelet and DTW (Dynamic Time Warping) classification techniques on brain signal time-series shows the potential to identify neuro-biological phenomena that can proactively signal a disease or disorder. EEG bandwidth and frequency-specific data have also been categorized as machine learning algorithms and have shown accurate patterns and trends in measuring cognitive functions of scientific, biological and academic importance.

关键词： Electroencephalography Feature extraction Diseases machine learning Task analysis Time series analysis machine learning algorithms Time-series machine learning data classification cognitive science shapelets DTW neurophysiological brain signal

来源：评论

学校读者我要写书评

暂无评论

Using machine learning algorithms to map the groundwater recharge potential zones

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020年 265卷 110525-110525页

作者： Pourghasemi, Hamid Reza Sadhasivam, Nitheshnirmal Yousefi, Saleh Tavangar, Shahla Nazarlou, Hamid Ghaffari Santosh, M. Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Bharathidasan Univ Sch Earth Sci Dept Geog Tiruchirappalli 620024 Tamil Nadu India AREEO Soil Conservat & Watershed Management Res Dept Chaharmahal & Bakhtiari Agr & Nat Resources Res & Shahrekord Iran Tarbiat Modare Univ Fac Nat Resources & Marine Sci Dept Watershed Management Engn Tehran Iran Howard Univ Dept Civil & Environm Engn Washington DC 20059 USA China Univ Geosci Beijing Sch Earth Sci & Resources Beijing 100083 Peoples R China Univ Adelaide Dept Earth Sci Adelaide SA 5005 Australia

Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model;DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources.

关键词： Groundwater recharge machine learning algorithms Variable importance LASSO

来源：评论

学校读者我要写书评

暂无评论

Measurement of Users' Well-Being Through Domotic Sensors and machine learning algorithms

引用

IEEE SENSORS JOURNAL 2020年第14期20卷 8029-8038页

作者： Casaccia, Sara Romeo, Luca Calvaresi, Andrea Morresi, Nicole Monteriu, Andrea Frontoni, Emanuele Scalise, Lorenzo Revel, Gian Marco Univ Politecn Marche Dept Ind Engn & Math Sci I-60121 Ancona Italy Univ Politecn Marche Dept Informat Engn I-60121 Ancona Italy

This paper proposes a specific domotic sensor network to measure the well-being of elderly people in private home environments through machine learning (ML) algorithms trained with daily surveys. The tests have been conducted in 5 apartments lived by 8 older people where the non-obtrusive sensor network is installed. Two ML algorithms are compared, Random Forest (RF) and Regression Tree (RT), such that to verify whether the users' well-being is encoded in behavioural patterns obtained from the domotic data. These data are used to measure users' well-being and compared with three reference indices obtained through a daily survey: a physical (Phy), a mental (Mind) and a general health index (Avg). The extracted indices from the daily survey are used to train ML algorithms in the estimation of user's well-being for users that live alone (single-resident) or with others (multi-resident). Single-house and multi-house procedures are tested, both to extract a user-specific behaviour, and assess whether the model is able to generalise across different users and environments. Results show that the RF algorithm provides better performance than the RT algorithm in predicting the level of well-being with a Mean Absolute Error in the multi-house procedure of 32%, 13% and 17% for the Avg, Mind and Phy indices, respectively.

关键词： Monitoring machine learning algorithms Temperature sensors Temperature measurement Prediction algorithms Intelligent sensors Aging sensors smart home machine learning measurement uncertainty

来源：评论

学校读者我要写书评

暂无评论

Modelling human health vulnerability using different machine learning algorithms in stone quarrying and crushing areas of Dwarka river Basin, Eastern India

引用

ADVANCES IN SPACE RESEARCH 2020年第6期66卷 1351-1371页

作者： Mandal, Indrajit Pal, Swades Univ Gour Banga Dept Geog Malda India

Stone dust induced risk and vulnerability in the developing nations is very thought-provoking and therefore is necessary to address scientifically to manage it. Identifying vulnerable areas using the robust method is preliminary and essential steps. The present study has intended to delineate Human health vulnerability models (HHVMs) using machine learning algorithms and justify whether ensemble prediction can provide improved results in stone quarry and crushing dominated Middle catchment of Dwarka river basin. Support Vector machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Reduced Error Pruning (REP) Tree, Gaussian Process algorithms and ensemble prediction have been applied for HHV modelling. Total 6.9 to 7.37% in cluster 1, 9.11 to 9.6% in cluster 2, 19.63 to 26.79% and 5.34 to 12.85% areas in cluster 3 and 4 is found into very high human health vulnerable class as per the five applied models. REPTree model is found consistent for predicting vulnerability in case of all the clusters followed by GP model. As per the result of RMSE, the RF model is appeared as the most consistent model followed by REPTree. Some of the ensemble prediction models are potential for yielding improved result than individual algorithm. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Dust emission Human health vulnerability Risk and exposure factors machine learning algorithms Ensemble prediction

来源：评论

学校读者我要写书评

暂无评论

Deforestation probability assessment using integrated machine learning algorithms of Eastern Himalayan foothills (India)

引用

RESOURCES CONSERVATION & RECYCLING ADVANCES 2022年 14卷

作者： Saha, Soumik Bhattacharjee, Sumana Shit, Pravat Kumar Sengupta, Nairita Bera, Biswajit Sidho Kanho Birsha Univ Dept Geog Ranchi RdPO Purulia Sainik Sch Purulia 723104 India Univ Calcutta Jogesh Chandra Chaudhuri Coll Dept Geog 30 Prince Anwar Shah Rd Kolkata 700033 India Vidyasagar Univ Raja Narendralal Khan Womens Coll Autonomous PG Dept Geog Midnapore 721102 India Diamond Harbour Womens Univ Dept Geog Sarisha 743368 India

The significant biodiversity rich Jaldapara National Park is situated at Terai-Dooars region of Eastern Himalayan foothill. This study attempts to identify the deforestation probable zones at Jaldapara national park and its surroundings applying five different machine learning algorithms (SVM, NB, RF, DT and ANN). Results show that the northern and middle sections are being faced by high rate of deforestation due to large scale human encroachment, poaching and timber trafficking. Result also illustrates that support vector machine (SVM) brings more accuracy compared with other models. These deforestation probable models are validated through receiver operation characteristics, efficiency, sensitivity and specificity measurement. Area under curve (AUC) value of these models is 0.907, 0.885, 0.825, 0.846 and 0.876 respectively. The novelty of this research is that previously, such machine learning methods (with high precision) have not applied to examine the deforestation probability in this region of Himalayan foothill.

关键词： Jaldapara nationalpPark Deforestation probability machine learning algorithms AUC value Support vector machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：