• Title/Summary/Keyword: Text Signal

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Traffic Signal Recognition System Based on Color and Time for Visually Impaired

  • P. Kamakshi
    • International Journal of Computer Science & Network Security
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    • v.23 no.4
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    • pp.48-54
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    • 2023

  • Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

  • https://doi.org/10.22937/IJCSNS.2023.23.4.7 인용 PDF

Analysis of Adverse Drug Reaction Reports using Text Mining (텍스트마이닝을 이용한 약물유해반응 보고자료 분석)

  • Kim, Hyon Hee;Rhew, Kiyon
    • Korean Journal of Clinical Pharmacy
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    • v.27 no.4
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    • pp.221-227
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    • 2017

  • Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

  • https://doi.org/10.24304/kjcp.2017.27.4.221 인용 PDF KSCI

A Study on Text Pattern Analysis Applying Discrete Fourier Transform - Focusing on Sentence Plagiarism Detection - (이산 푸리에 변환을 적용한 텍스트 패턴 분석에 관한 연구 - 표절 문장 탐색 중심으로 -)

  • Lee, Jung-Song;Park, Soon-Cheol
    • Journal of Korea Society of Industrial Information Systems
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    • v.22 no.2
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    • pp.43-52
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    • 2017

  • Pattern Analysis is One of the Most Important Techniques in the Signal and Image Processing and Text Mining Fields. Discrete Fourier Transform (DFT) is Generally Used to Analyzing the Pattern of Signals and Images. We thought DFT could also be used on the Analysis of Text Patterns. In this Paper, DFT is Firstly Adapted in the World to the Sentence Plagiarism Detection Which Detects if Text Patterns of a Document Exist in Other Documents. We Signalize the Texts Converting Texts to ASCII Codes and Apply the Cross-Correlation Method to Detect the Simple Text Plagiarisms such as Cut-and-paste, term Relocations and etc. WordNet is using to find Similarities to Detect the Plagiarism that uses Synonyms, Translations, Summarizations and etc. The Data set, 2013 Corpus, Provided by PAN Which is the One of Well-known Workshops for Text Plagiarism is used in our Experiments. Our Method are Fourth Ranked Among the Eleven most Outstanding Plagiarism Detection Methods.

  • https://doi.org/10.9723/jksiis.2017.22.2.043 인용 PDF KSCI

A Study on the Impact of Speech Data Quality on Speech Recognition Models

  • Yeong-Jin Kim;Hyun-Jong Cha;Ah Reum Kang
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.1
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    • pp.41-49
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    • 2024

  • Speech recognition technology is continuously advancing and widely used in various fields. In this study, we aimed to investigate the impact of speech data quality on speech recognition models by dividing the dataset into the entire dataset and the top 70% based on Signal-to-Noise Ratio (SNR). Utilizing Seamless M4T and Google Cloud Speech-to-Text, we examined the text transformation results for each model and evaluated them using the Levenshtein Distance. Experimental results revealed that Seamless M4T scored 13.6 in models using data with high SNR, which is lower than the score of 16.6 for the entire dataset. However, Google Cloud Speech-to-Text scored 8.3 on the entire dataset, indicating lower performance than data with high SNR. This suggests that using data with high SNR during the training of a new speech recognition model can have an impact, and Levenshtein Distance can serve as a metric for evaluating speech recognition models.

  • https://doi.org/10.9708/jksci.2024.29.01.041 인용 PDF HTML