Earticle

Visual object recognition has been growing quickly in the field of computer science, as it can be applied to such diverse areas as robotics, smartphones, and artificial intelligence. Although there have been a countless number of researches and methods to develop an effective visual object recognition software, there have been only few attempts to combine visual object recognition with data science. Data science analyzes large volumes of data in different forms and derives useful information from the results. In the experiment, this study developed a specific method to generate sentences that describe what’s happening in a picture by combining visual object recognition software with text mining technologies. This study utilizes CamFind to identify a number of objects in a picture and applies NodeXL from Microsoft Excel to obtain online data from Twitter, a social network service. Information from the analysis of texts induced from objects is used to make stories for the whole image.

As the value of Big Data was recognized as important, the government, public institutions and private companies began to pay attention to Big Data. Unlike the past, there are diverse data sources, and with the emergence of various planning and analysis techniques based on the convergence of these data, Big Data is sure to become the foundation for the creation of new advanced information and advanced decision-making. This study is to find an alternative to the atmospheric fine dust concentration that is not improved despite various measures. The purpose of this study is to exploit the public data of the Seoul Metropolitan City in order to reduce the air pollution concentration in Seoul. The study was conducted through open source R for Seoul public data analysis. As a result, policy alternatives to activate natural gas vehicles were derived.

In this paper, we propose the compared performance of normalization methods-based machine learning classification some techniques for NG leak prediction. The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. The proposed method is OrdinalEncoder(OE) based K-means clustering and OE transformation based SVM and MLP classifications for predicting NG leak. We have shown that our proposed OE based SVM method accuracy 97.82%, F1-score 98.54% and both of two normalization based MLP accuracy and F1-score also more than 96% which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

The integration of deep learning techniques in the field of audio signal processing has marked a significant leap forward in the capability to analyze and classify complex sounds, including the nuanced and information-rich cries of infants. Deep learning's promise in this domain lies in its potential to decipher the subtle cues contained within these cries, offering insights into an infant's health, emotional state, and developmental needs. This potential application stands at the intersection of technology and healthcare, promising to enhance our understanding and response to the needs of the youngest members of society. This study experimentally demonstrates that a convolutional neural network–based audio classification model effectively learns discriminative spectral and temporal features from audio signals. Experimental results show that the proposed convolutional neural networks architecture achieves significantly higher classification accuracy than traditional machine-learning baselines, particularly when trained on spectrogram-based representations. The findings confirm that deep learning models not only improve overall performance but also provide robust generalization across different audio classes and noisy conditions.

With the development of computer vision technology utilizing GPUs, objects in video images are being detected in real-time and utilized in various fields. The emergence of CNN technology for detecting objects in video images has made significant progress in object detection research. CNNs have evolved through R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, etc., improving the accuracy and performance of object detection. While CNN-based technology accurately detects objects, detection time is lengthy, making real-time detection challenging. However, the YOLO technology, which enables real-time detection with a fast detection speed, was first proposed, and the accuracy has been greatly improved with the recent development of YOLOv9. Additionally, YOLO technology can operate on low-end boards such as Raspberry Pi or Jetson Nano, so it is used in various fields. Recently, YOLO technology can be used not only for image processing but also for security monitoring services, vehicle access control, and crack detection on roads through CCTV.