Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing

Khan, Talha Ahmed; Alam, Muhammad Mansoor; Shahid, Zeeshan; Su’ud, Mazliham Mohd

doi:10.1109/access.2020.3038812

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…The reason lies in the following two aspects: a) The NN is highly representative due that it is a R&D hotspot in the AI field since the 1980s and there are a lot of STAs (Chuah et al, 2002), and has successfully solved many practical problems in many fields, including pattern recognition, intelligent robot, automatic control, prediction and estimation, biology, medicine, economy, etc. (Khan et al, 2020); b) The NN field has developed into the expansion stage, which is the top level among the 28 AI subfields (Table 7). Therefore, let i = 4 , j = 2 , s ij = D .…”

Section: Empirical Analysis and Discussionmentioning

confidence: 99%

Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network

et al. 2022

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This article aims to analyze the correlation status and development trend among science, technology and industry in the Artificial Intelligence (AI) subfield. First, it constructs the classification system and retrieval strategy for the AI subfield, and performs data cleaning and data finishing. Second, it designs the criteria and judgment method of the Industry Development Stages (IDS) in the AI subfield with the Delphi survey method. Third, it designs the monitoring method and model of the Science-Technology-Industry (STI) correlation with the methods of scientometrics and clustering, which involves three dimensions: science and IDS, technology and IDS, science & technology clustering and IDS; especially the monitoring with the global and national perspectives. Finally, it conducts empirical application in the field of neural network. The results illustrate that the correlation monitoring method can be conducive to grasp accurately the development opportunity and trend, the advantage subfield, and the development status of Science and Technology (S&T) for the dominant countries. The United States leads the global development in the empirical field. The proposed correlation monitoring method in this article can also be applied in other fields.

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Section: Empirical Analysis and Discussionmentioning

confidence: 99%

Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network

et al. 2022

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“…This model utilizes a dataset of 100,000 open-source Weibo comments labeled with emotion categories The data is split into a training set and a test set (8:2 ratio) for model development and validation. By comparing random forest and support vector machine algorithms, support vector machine is chosen as the foundation for the model algorithm [30][31][32][33][34]. NER rule based approach depends on defined boundaries, these techniques help to achieve efficiency and better accuracy rate but cannot be used with other domains [35].…”

Section: Existing Approachesmentioning

confidence: 99%

Sentiment Analysis using Support Vector Machine and Random Forest

Ahmed Khan,

Sadiq,

Shahid

et al. 2024

JIWE

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Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain. Based on the results obtained, two machine learning algorithms named as Random Forest and SVM were evaluated based on their accuracy in a classification task. The Random Forest algorithm achieved an accuracy of 0.78564, while SVM outperformed it slightly with an accuracy of 0.80394. Both Random Forest and SVM have demonstrated their strengths in achieving respectable accuracies in the given classification task. These results suggest that SVM, with its slightly higher accuracy of 0.80394, may be a more suitable choice when accuracy is the primary concern. However, the basic configuration need and characteristics of the problem at hand should be considered when choosing the better algorithm with better results.

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“…Table 1 organizes relevant papers, including 1) the application area including runoff, river, sewage, irrigation, container, and reservoir, 2) water movement characteristics (or "water status") including standing (standing water), disturbance (standing water with disturbance), flow (calm flow), and turbulence (turbulent flow), 3) water quality including clean and turbid, 4) installation height including low and high (as previously defined), and 5) the sensor used. 12 Sewage Not provided Turbidity Not provided HC-SR04 Sabre et al 13 Runoff Not provided Turbidity Low HC-SR04 Hasbullah et al 14 Runoff Not provided Turbidity Low HC-SR04 Khan et al 15 Runoff Not provided Turbidity Low HC-SR04 Tejaswitha et al 16 Runoff Not provided Turbidity Not provided HC-SR04 Mastor et al 17 Runoff Not provided Turbidity Not provided HC-SR04 Khairudin et al 18 Container Not provided Clean Not provided HC-SR04 Khairunnas et al 19 Container Disturbance Clean Low HC-SR04 Yudhana et al 20 Container Disturbance Clean Low HC-SR04 Amin et al 21 Reservior Disturbance Clean Low HC-SR04 Saragih et al 22 Reservior Disturbance Clean High HC-SR04 Rama et al 23 Irrigation Standing Turbidity Low HC-SR04 Desnanjaya et al 24 Irrigation Standing Turbidity Low HC-SR04 Azmi et al 25 Container 59 Runoff Flow Turbidity Not provided HC-SR04 Noar et al 60 River Flow Turbidity Low HC-SR04 Finawan et al 61 River Flow Turbidity Low HC-SR04 Septiana et al 62 River Flow Clean Low HC-SR04 Satria et al 63 River Flow Clean Low HC-SR04 Sai et al 64 Runoff Flow Turbidity Low HC-SR04 Khan et al 65 Runoff Flow Turbidity Low HC-SR04 Sumitra et al 66 Runoff Flow Turbidity Low HC-SR04 Olesnanikova et al 67 River Turbulence Clean High HC-SR04 Yahaya et al 68 River Turbulence Clean Not provided HC-SR04 Satria et al 69 Runoff Turbulence Clean Low HC-SR04 Sulistyowati et al 70 River Flow Clean Low HY-SRF04 Yoeseph et al 71 Runoff Not provided Turbidity Low HY-SRF05 Mulyana et al 72 Container Standing Turbidity High HY-SRF05 Riv...…”

Section: Overview Of Ultrasonic Sensor Use For Water and Hydraulic Sy...mentioning

confidence: 99%

Scientific evaluation of ultrasonic sensors for use in applications of water and hydraulic monitoring

Zhang

Flanigan

Ren

2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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As easy-to-deploy, off-the-shelf sensors decrease in cost and increase in accessibility through their integration with well-documented, entry-level electronics platforms, low-quality sensors are increasingly being inappropriately used to characterize physical and natural processes under varying environmental and operational conditions. This is notably occurring across water and hydraulic system applications, which necessitates measuring water levels using ultrasonic sensors. To lay a roadmap for future water and hydraulic system monitoring research and implementation, there is a need to develop a well-informed mapping between sensors and application areas for which the use of the sensors is deemed appropriate. In this work, we identify commonly used ultrasonic sensors, develop systematic experimental setups to simulate their use in common application areas, and evaluate their accuracy under varying conditions and parameters. From the results of these experiments, we present a suggested mapping between sensors and application areas/conditions for which the use of the sensors is appropriate, in addition to limitations placed on each sensor-application pairing are identified.

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Prior Recognition of Flash Floods: Concrete Optimal Neural Network Configuration Analysis for Multi-Resolution Sensing

Cited by 8 publications

References 31 publications

Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network

Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network

Sentiment Analysis using Support Vector Machine and Random Forest

Scientific evaluation of ultrasonic sensors for use in applications of water and hydraulic monitoring

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