Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

Purwandari, Kartika; Sigalingging, Join W. C.; Cenggoro, Tjeng Wawan; Pardamean, Bens

doi:10.1016/j.procs.2020.12.006

Cited by 40 publications

(18 citation statements)

References 20 publications

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“…If it exists, select the corresponding word vector and represent it with C i , otherwise, set the corresponding word vector C i = 0. After finding the word vector corresponding to each word, stack each word vector to form a word vector characteristic matrix C, whose size is n × m. Each line of C represents the word vector corresponding to a word in the corpus, which can be expressed as [19,20]. The LSTM network structure is shown in Figure 2.…”

Section: Text Word Vector Representation the Corpus Is Trained Withmentioning

confidence: 99%

Question Text Classification Method of Tourism Based on Deep Learning Model

Luo

Zhang²

2022

Wireless Communications and Mobile Computing

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The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and F value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.

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Section: Text Word Vector Representation the Corpus Is Trained Withmentioning

confidence: 99%

Question Text Classification Method of Tourism Based on Deep Learning Model

Luo

Zhang²

2022

Wireless Communications and Mobile Computing

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“…Therefore, the accuracy of classification will become the key factor to ensure the rationality of evaluation. When the data is highly unbalanced, the results of performance prediction and evaluation may be more applicable [25,26]. In the process of sports prediction, it is key to save training data efficiently.…”

Section: Solutions To Research Problemsmentioning

confidence: 99%

Sports Performance Prediction Based on Chaos Theory and Machine Learning

Sun

2022

Wireless Communications and Mobile Computing

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In order to combine chaos theory and machine learning technology to predict sports performance, a research on sports performance prediction based on chaos theory and machine learning is proposed. This paper takes the sports performance as the goal to predict the data; introduce the chaos theory algorithm, and combine the neural network system and particle swarm optimization algorithm to actively train sports results and ensure the quality of performance prediction. The comparison between shot put data prediction and real data shows that the prediction results given by the model have little deviation and can provide technical services for performance prediction for special sports training. When predicting the sports performance of college students, the prediction accuracy of sports performance of each subject is no less than 90%, which proves that this system can be used in college sports management. The delay time of the model data is short, which is mainly related to the correlation coefficient. When the coefficient is determined to be 0.02, the prediction delay time is 5 s, which can effectively complete the prediction and analysis of sports performance. The combined model has various technical advantages such as chaos theory, neural system, and particle optimization. It has strong sports performance prediction ability and can provide technical support for performance prediction for athletes’ training, college sports management, and other related sports industries.

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“…Computer vision is one of the artificial intelligence that can automatically extract patterns in images by providing training images for the algorithm to recognize and predict a new image [16]. One of the most popular AI techniques is Convolutional Neural Networks (CNN) that finds a wide variety of applications in computer vision, signal processing, weather prediction, and more [17][18][19][20]. CNN can learn complex and inherent patterns within an image by expanding them into arrays represented by pixel intensity (0 -255) for every point in the image.…”

Section: Computer Vision In Palm Oil Plantation Sectormentioning

confidence: 99%

Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

Firmansyah

Suparyanto

Hidayat

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The effectiveness and efficiency of the operation of oil palm plantations are considered to be the most crucial factor to develop the productivity and profitability of the palm oil business. One of the major obstacles for the plants to optimally produce crops based on their capacity is caused by the presence of noxious weeds in the plantation area. However, weed control via chemical processes may potentially harm the surrounding environment if it is not properly managed. Therefore, an automatic system to assist the farmers to identify and control the weeds is required to minimize harmful impacts on the environment. Machine Learning (ML) and Artificial Intelligence (AI)-based systems provide powerful tools to perform such tasks. In this work, we aim for an ML-based system design to perform an automatic weed recognition task. The methodology can provide an effort for environmental sustainability in oil palm plantations. The weed identification involves the description, the local names, and tolerance class of the weeds as well as suggestions to control them. The flow of this work consists of weed and herbicide data acquisition, data labeling, model configurations, and data training. Further, the proposed system can be adopted as an android-based application in mobile devices that can deploy the trained model to predict weed category in both real-time and non-real-time tasks.

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Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

Cited by 40 publications

References 20 publications

Question Text Classification Method of Tourism Based on Deep Learning Model

Question Text Classification Method of Tourism Based on Deep Learning Model

Sports Performance Prediction Based on Chaos Theory and Machine Learning

Real-time Weed Identification Using Machine Learning and Image Processing in Oil Palm Plantations

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