Classification using semantic feature and machine learning: Land-use case application

Elmannai, Hela; Algarni, Abeer D.

doi:10.12928/telkomnika.v19i4.18359

Cited by 10 publications

(11 citation statements)

References 32 publications

(54 reference statements)

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“…The system dynamics model, which is more applicable in analyzing the internal structure and quantity prediction, can reflect the complex behavior of the land system from a macroscopic perspective and is better applied in the scenario simulation study of land-use change. Still, the model lacks spatial analysis tools, so it is difficult to realize the simulation of land-use spatial distribution [ 8 ].…”

Section: Related Workmentioning

confidence: 99%

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

Yang

Wang

Jia-jun

et al. 2022

Computational Intelligence and Neuroscience

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In this article, the principles of the gray model and BP neural network model are analyzed, and the characteristics of land-use change and spatial and temporal distribution are studied in-depth, and at the same time, to explore the influence of land-use change on ESV, the relationship between the two is analyzed using gray correlation degree, and a mathematical model is constructed to maximize the benefits of the regional system, coupling economic and ecological benefits, combined with Geo SOS-FLUS model to achieve the optimization of land use. This article constructs a combined prediction model of a gray neural network. The gray differential equation parameters correspond to the weights and thresholds of the neural network, and the optimized parameters are determined by training the neural network to make it stable. Then the training results of the BP neural network are fitted with the results obtained from the gray GM (1.1) model. Finally, the prediction results of the three models, gray GM (1.1), BP God Meridian, and gray neural network model, are compared and analyzed. The global spatial autocorrelation and local spatial aggregation patterns of regional soil erosion and its erosion factors are analyzed using the Exploratory Spatial Data Analysis (ESDA) method in spatial measurement theory.

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Section: Related Workmentioning

confidence: 99%

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

Yang

Wang

Jia-jun

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Some government departments not only consider the level of land use and input-output efficiency, but also consider whether urban land is low-utility land in terms of land layout, level of supporting facilities, and ecological and environmental protection. The article [ 10 ] evaluates the efficiency of industrial land use in terms of land use output and social services based on the micro-plot scale and explores the relationship between land use structure and its efficiency. The literature [ 11 ] studied the effect of far-left-turn traffic implementation at signalized intersections and proposed a microscopic traffic model applicable to typical urban areas, and evaluated the left-turn organization of different signal-controlled intersections.…”

Section: Related Workmentioning

confidence: 99%

“…e article [10] evaluates the efficiency of industrial land use in terms of land use output and social services based on the micro-plot scale and explores the relationship between land use structure and its efficiency. e literature [11] studied the effect of far-leftturn traffic implementation at signalized intersections and proposed a microscopic traffic model applicable to typical urban areas, and evaluated the left-turn organization of different signal-controlled intersections.…”

Section: Related Workmentioning

confidence: 99%

The Big Data Model for Urban Road Land Use Planning Is Based on a Neural Network Algorithm

Zhang

2022

Computational Intelligence and Neuroscience

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The spatial differentiation of land use induces traffic demand and guides the construction of traffic supply; traffic conditions are an important influencing factor in determining the nature of land use, and there is a close interaction between the two. This study uses a neural network-based approach at the urban grid level to portray representative phenomena of urban development and analyze the interaction between transportation and land use. The results reflect the model’s effective simulation of urban laws, and the case study reveals the differences in the laws of different cities, to guide the benign development of cities and transportation. This article firstly conducts a study on the theoretical foundation; compares the development history, planning, and design methods and practical experience of road planning and resilient planning; summarizes the experience of resilient road system design; and analyzes the future development trend, based on the above basic theoretical research, to develop research ideas and methods. Secondly, the scenario analysis method is explicitly applied to analyze various scenarios that may occur in the future development process of simulated urban roads and rank the scenarios based on the probability of occurrence. For the impact of traffic on land use, the concepts of vitality and potential are introduced, and a multidimensional long and short-term memory network (MDLSTM) model is established. The model takes into account land use lags and potential transfer and has relatively higher prediction accuracy. The results show that larger cities with urban dominant industries and tertiary industries also have higher land use potential and the more significantly influenced by traffic.

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“…Different methods of AI can be used in this field, such as deep learning and machine learning, due to their efficiency in copying the human behavior, in which the diagnosing is accursed [10], [11]. The machine learning technique is based on building more than one model that can be learned using a dataset related to the subject [12], [13]. These models provide the system with classified and arranged results.…”

Section: Introductionmentioning

confidence: 99%

IgG-IgM antibodies based infection time detection of COVID-19 using machine learning models

Khudhur

2022

TELKOMNIKA

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Over the last two years, most scientists have been researching the solution to the pandemic coronavirus disease 2019 . So, the effective inspection and the rapid diagnosis of COVID-19 provide a mitigation ability to the burden on healthcare systems. These research works focus on detecting and knowing the history of infection in terms of time and developed symptoms. In infections detection, artificial intelligence (AI) technologies increase the accuracy and efficiency of the adopted detection methods. These methods will aid the medical staff in classifying patients, essentially when there is a healthcare resources shortage. This paper proposed machine learning-based models for detecting the time of COVID-19 infection in weeks using the laboratory factors of detected antibodies immunoglobulins G and immunoglobulins M (IgG-IgM). This test is common and helpful in diagnosing the suspected patients who held a negative result for the reverse transcription-polymerase chain reaction (RT-PCR) test. The proposed models consider two machine learning models adopting root mean square error (RMSE) and mean absolute error (MAE) factors. The results show acceptable efficiency of performance that ranges from 80% to 100% for pointing the patient in any week of infection, to reduce the likelihood of transmitting the infection from patients who have developed symptoms but with false-negative RT-PCR test.

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Classification using semantic feature and machine learning: Land-use case application

Cited by 10 publications

References 32 publications

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

An Improved Gray Neural Network Method to Optimize Spatial and Temporal Characteristics Analysis of Land-Use Change

The Big Data Model for Urban Road Land Use Planning Is Based on a Neural Network Algorithm

IgG-IgM antibodies based infection time detection of COVID-19 using machine learning models

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