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2022 ◽  
Vol 5 (1) ◽  
pp. 116-123
Author(s):  
Yola Tri Handika ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo

Hoax news (hocus to trick) has a very big influence in disseminating information, especially in the world of social media. News has an important impact on social and political conditions, and news can move the economy of a country. For this reason, it is necessary to have an analysis to classify hoax news and not hoaxes, and have high accuracy in classifying the news. In this study, two methods were used as a comparison in achieving high accuracy, namely the Naïve Bayes method which is famous for having high accuracy in classification with little data, and the C.45 method which can minimize noise in the data. The data used are 300 articles with 10 topics which contain hoax and non-hoax news. The data is obtained from the internet through social media, such as Twitter, Instagram and Facebook. Testing using the Naïve Bayes method has a higher accuracy than the C.45 method. The amount of data used has a major influence on the test results, if more data enters the training stage, then this study will have higher accuracy. However, the results of this test can be recommended to increase accuracy in the construction of a hoax news detection system.


2022 ◽  
Vol 2022 ◽  
pp. 1-15
Author(s):  
Lingfei Mo ◽  
Hongjie Yu ◽  
Wenqi Hua

Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results. However, most human physical activity identification models can only be trained based on labeled data, and it is difficult to obtain enough labeled data, which leads to weak generalization ability of the model. A Pruning Growing SOM model is proposed in this paper to address the limitations of small-scale labeled dataset, which is unsupervised in the training stage, and then only a small amount of labeled data is used for labeling neurons to reduce dependency on labeled data. In training stage, the inactive neurons in network can be deleted by pruning mechanism, which makes the model more consistent with the data distribution and improves the identification accuracy even on unbalanced dataset, especially for the action categories with poor identification effect. In addition, the pruning mechanism can also speed up the inference of the model by controlling its scale.


2022 ◽  
Vol 355 ◽  
pp. 03027
Author(s):  
Ziliang Huang ◽  
Rujing Wang ◽  
Liusan Wang ◽  
Yue Teng ◽  
Shijian Zheng

The identification of seed quality is very important for which the quality of seed is crucial to the yield and quality of crops. There are two main problems with the acquisition and identification of cracks inside corn seed. One is that most of the methods of near-infrared spectroscopy or X-ray are used to obtain images of cracks inside the seed, the acquisition equipment is expensive and the operation is complicated. The other is the identification of crack images, and the traditional image processing method is usually used which requires professionals to design different model parameters each time, resulting in poor model robustness and low model accuracy. In this study, we originally proposed a simple but effective method to obtain the picture of corn seed internal cracks, which is combined with visible light transmission and ordinary camera acquisition method. We also proposed using the transfer learning methods not only solving the problem of the small scale of our corn seed internal cracks dataset but also avoiding extracting features manually. Our proposed method achieved a promising result, which is able to correctly identify the cracked and intact corn seed 100% in our training stage and testing stage.


Author(s):  
Rosalia Arum Kumalasanti ◽  

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.


2021 ◽  
Vol 29 (85) ◽  
pp. 42-45
Author(s):  
Magdalena Lelonek ◽  
Piotr Unierzyski ◽  
Grzegorz Lelonek

The first tennis training stage, which usually takes place between four and six years of age, cannot simply follow an adult training regime with quantitatively reduced loads. Training should account for children’s cognitive, emotional, social, physical and motor development. This article highlights the cornerstones of early tennis teaching, including fundamental motor skills, which help to develop more complex motor actions, and motor abilities, especially strength fitness, which determines posture, jumping, running and throws. This is achieved through fun plays and games, which should include various coordination tasks providing motor experiences and develop more complex actions in future.


2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Qiang Lin ◽  
Ancheng Luo ◽  
Yan Zhang ◽  
Yunlong Wang ◽  
Zhiwei Liang ◽  
...  

Domestic sewage in rural regions is mainly treated by small-scale treatment terminals in China. The large quantities and high dispersion of these terminals render the chemical measurement of effluent to be a time and energy intensive work and further hinder the efficient surveillance of terminals’ performance. After a thorough investigation of 136 operating terminals, this study successfully employs two artificial neural network (ANN) models to predict effluent total nitrogen (TN) and COD (R2 both higher than 0.8) by setting some easily detectable parameters, e.g., pH and conductivity, as inputs. To prevent ANN models getting stuck on local optima and enhance the model performance, genetic algorithm (GA) and particle swarm optimization (PSO) are introduced into ANN, respectively. By comparison, ANN-PSO excels in modelling both TN and COD. The root mean square error (RMSE) and R2 of ANN-PSO in modelling TN are 9.14 and 0.90, respectively, in the training stage, and 11.54 and 0.90, respectively, in the validation stage. The RMSE and R2 of ANN-PSO in modelling COD are 22.10 and 0.90, respectively, in the training stage, and 26.57 and 0.85, respectively, in the validation stage. This is the first study to provide performance prediction models that are available for different terminals. Two established ANN-PSO models show great practical significance in monitoring huge amounts of terminals despite the slight sacrifice of models’ accuracy caused by the great heterogeneity of different terminals.


Information ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Rong Du ◽  
Weiwei Li ◽  
Shudong Chen ◽  
Congying Li ◽  
Yong Zhang

Underwater image enhancement recovers degraded underwater images to produce corresponding clear images. Image enhancement methods based on deep learning usually use paired data to train the model, while such paired data, e.g., the degraded images and the corresponding clear images, are difficult to capture simultaneously in the underwater environment. In addition, how to retain the detailed information well in the enhanced image is another critical problem. To solve such issues, we propose a novel unpaired underwater image enhancement method via a cycle generative adversarial network (UW-CycleGAN) to recover the degraded underwater images. Our proposed UW-CycleGAN model includes three main modules: (1) A content loss regularizer is adopted into the generator in CycleGAN, which constrains the detailed information existing in one degraded image to remain in the corresponding generated clear image; (2) A blur-promoting adversarial loss regularizer is introduced into the discriminator to reduce the blur and noise in the generated clear images; (3) We add the DenseNet block to the generator to retain more information of each feature map in the training stage. Finally, experimental results on two unpaired underwater image datasets produced satisfactory performance compared to the state-of-the-art image enhancement methods, which proves the effectiveness of the proposed model.


2021 ◽  
Vol 5 (1) ◽  
pp. 981
Author(s):  
Basuki Basuki ◽  
Sukron Romadhona ◽  
Listya Purnamasari ◽  
Vega Kartika Sari

ABSTRAKLahan pertanian mengalami penurunan kesuburan tanah dengan indikasi nilai C-Organik < 1% sangat luas, termasuk di Desa Sekarputih Kabupaten Bondowoso. Masyarakat Desa Sekarputih sebagian besar bermata pencaharian sebagai petani dan peternak. Survei awal menunjukkan bahwa kotoran sapi yang dihasilkan oleh ternak belum dimanfaatkan secara optimal. Banyak kotoran sapi yang dibiarkan begitu saja sehingga menimbulkan masalah lingkungan seperti bau yang menyengat. Tujuan dari program pengabdian ini adalah sosialisasi dan pelatihan pembuatan pupuk organik yang berasal dari kotoran sapi sebagai alternatif untuk meningkatkan bahan organik tanah dan mengurangi permasalahan lingkungan. hasil kegiatan pengabdian menunjukkan bahwa peserta sangat aktif dan antusias dalam bertanya dan menjawab pertanyaan dalam kegiatan sosialisasi, dan antusias aktif dalam praktek langsung pada tahap pelatihan. Kata kunci: pelatihan; pupuk organic; kotoran sapi; ternak. ABSTRACTAgricultural land experienced degradation of soil fertility with indicated C-Organic value < 1% very widely, including in Sekarputih Village, Bondowoso Regency. The people of Sekarputih village mostly make a living as farmers and ranchers. The initial survey shows that cow dung produced by livestock has not been utilized optimally. A lot of cow dung is left alone which causes environmental problems such as a strong odor. The purpose of this service program is socialization and training on the manufacture of organic fertilizer derived from cow dung as an alternative to increasing soil organic matter and reducing environmental problems. the results of the service activities showed that the participants were very active and enthusiastic about asking and answering questions in socialization activities, and enthusiastically active in direct practice at the training stage. Keywords: training; organic fertilizer; cow manure; livestock.


PRIMO ASPECTU ◽  
2021 ◽  
pp. 60-65
Author(s):  
Elena V. Abramenko ◽  
Lilia A. Fedotova

This article substantiates the importance of professional socialization of students of a technical university. It is the student age, due to its age-related mental and personal characteristics, that all types of socialization occur intensively. Consequently, students are considered as the most favorable period for professional socialization. In addition, the article analyzes the scientific sources of literature in terms of the definition of the concept of "professional socialization". A huge role in the training of qualified personnel is played by higher education, which lays the basic foundation for the personal and professional development of the younger generation, in whose hands is the future of society and the country as a whole. It should be noted that thanks to the study of humanities at a technical university, it is possible to implement such methods of working with students that will ensure successful professional socialization of junior students at the training stage, form personal qualities and increase the level of professional training. Using the example of specific humanities disciplines, such as "Communication in professional activity", "Business communication" and "Psychology of professional activity", the article presents work with junior students, which forms the social formation of a student's personality and is part of the process of professional socialization.


2021 ◽  
Vol 11 (24) ◽  
pp. 11938
Author(s):  
Denis Zherdev ◽  
Larisa Zherdeva ◽  
Sergey Agapov ◽  
Anton Sapozhnikov ◽  
Artem Nikonorov ◽  
...  

Human poses and the behaviour estimation for different activities in (virtual reality/augmented reality) VR/AR could have numerous beneficial applications. Human fall monitoring is especially important for elderly people and for non-typical activities with VR/AR applications. There are a lot of different approaches to improving the fidelity of fall monitoring systems through the use of novel sensors and deep learning architectures; however, there is still a lack of detail and diverse datasets for training deep learning fall detectors using monocular images. The issues with synthetic data generation based on digital human simulation were implemented and examined using the Unreal Engine. The proposed pipeline provides automatic “playback” of various scenarios for digital human behaviour simulation, and the result of a proposed modular pipeline for synthetic data generation of digital human interaction with the 3D environments is demonstrated in this paper. We used the generated synthetic data to train the Mask R-CNN-based segmentation of the falling person interaction area. It is shown that, by training the model with simulation data, it is possible to recognize a falling person with an accuracy of 97.6% and classify the type of person’s interaction impact. The proposed approach also allows for covering a variety of scenarios that can have a positive effect at a deep learning training stage in other human action estimation tasks in an VR/AR environment.


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