“…Mahmood and Qasim [17] used the Support Vector Machine (SVM) and Naive Bayes Model (NBM) to study NPO [17]. Huang [18] obtained Microblog and blog data through a web crawler and studied the classification of NPO texts by using the SVM model [18]. Liu et al [19] analyzed the interaction between social media-based NPO analysis and government management.…”
The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.
“…Mahmood and Qasim [17] used the Support Vector Machine (SVM) and Naive Bayes Model (NBM) to study NPO [17]. Huang [18] obtained Microblog and blog data through a web crawler and studied the classification of NPO texts by using the SVM model [18]. Liu et al [19] analyzed the interaction between social media-based NPO analysis and government management.…”
The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.
“…Artificial intelligence has been recognized as a groundbreaking technology since the introduction of ML in the 2000s, since research began in 1956. Proper use of AI can be a very effective tool against a variety of viral infections[ 19 ]. When we lists actual and potential ways for AI to combat the epidemic, disease surveillance[ 20 ], risk prediction[ 21 ], medical diagnosis and screening[ 22 ], curative studies[ 23 ], virus modeling and analysis[ 24 ], and implementation of lockdown measures[ 25 ], which are shown in Fig.…”
Section: Related Workmentioning
confidence: 99%
“… Fig. 3 Applications of AI for virus disease impact management[ 19 ] …”
Section: Related Workmentioning
confidence: 99%
“…This wearable is characterized by being connected to an individual smartphone and periodically transmitting data by accessing the Internet. Artificial intelligence(AI) has been recognized as a breakthrough in 2000 since its inception in 1956, and if used correctly, it can be a very effective tool against various viral epidemics[ 19 ]. If we list effective ways for AI to combat epidemics, there are disease monitoring[ 20 ] and risk prediction[ 21 ], medical diagnosis and screening[ 22 ], treatment research[ 23 ], virus modeling and analysis [ 24 ], lockdown measures trial[ 25 ].…”
Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.
“…Due to the use of a harvester in the configuration of the CTL system, the trees are cut and sectioned into processed logs. Additionally, the timber without harvest residues, suitable for industrial processes, is arranged for extraction by means of a forwarder [9][10][11][12][13][14][15][16].…”
The correct capture of forest operations information carried out in forest plantations can help in the management of mechanized harvesting timber. Proper management must be able to dimension resources and tools necessary for the fulfillment of operations and helping in strategic, tactical, and operational planning. In order to facilitate the decision making of forest managers, this work aimed to analyze the performance of machine learning algorithms in estimating the productivity of timber harvesters. As predictors of productivity, we used the availability of hours of machine use, individual mean volumes of trees, and terrain slopes. The dataset was composed of 144,973 records, carried out over a period of 28 months. We tested the predictive performance of 24 machine learning algorithms in default mode. In addition, we tested the performance of blending and stacking joint learning methods. We evaluated the model’s fit using the root mean squared error, mean absolute error, mean absolute percentage error, and determination coefficient. After cleaning the initial database, we used only 1.12% to build the model. Learning by blending ensemble stood out with a determination coefficient of 0.71 and a mean absolute percentage error of 15%. From the use of data from machine learning algorithms, it became possible to predict the productivity of timber harvesters. Testing a variety of machine learning algorithms with different dynamics contributed to the machine learning technique that helped us reach our goal: maximizing the model’s performance by conducting experimentation.
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