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Journal articles on the topic 'Audio data mining'
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Xu, Shasha. "Effective Graph Mining for Educational Data Mining and Interest Recommendation." Applied Bionics and Biomechanics 2022 (August 12, 2022): 1–5. http://dx.doi.org/10.1155/2022/7610124.
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In order to fully understand and analyze the rules and cognitive characteristics of users’ learning methods and, with the assistance of Internet and artificial acquaintance technology, to emphasize the integrity and degree of personalized education, a personalized graph-learning-based recommendation system including user portraits is proposed. System raking of data layers, data analysis responses, and recommendations for sum beds are seamless and collaboratively combined. The data layer consists of user data and a design library containing scholarship materials, study materials, and price sets. The data analysis framework is captured by rest and energy data represented by basic information, learning behavior, etc. We can provide perceptual and visual learning audio feedback. And thus witness computing should convey users’ learning behavior rules through similarity analysis and mob algorithm. We further use TF-IDF to sequentially mine users’ resource priorities and always bind personalized learning suggestions. The system has been applied to an online education platform supported by artificial intelligence technique, which can provide instructors and students with personalized portraits. We also proposed to learn audio feedback and data consulting services, typically during the hard work phase of the assistant semester.
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Xu, Yanping, and Sen Xu. "A Clustering Analysis Method for Massive Music Data." Modern Electronic Technology 5, no. 1 (May 6, 2021): 24. http://dx.doi.org/10.26549/met.v5i1.6763.
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Clustering analysis plays a very important role in the field of data mining, image segmentation and pattern recognition. The method of cluster analysis is introduced to analyze NetEYun music data. In addition, different types of music data are clustered to find the commonness among the same kind of music. A music data-oriented clustering analysis method is proposed: Firstly, the audio beat period is calculated by reading the audio file data, and the emotional features of the audio are extracted; Secondly, the audio beat period is calculated by Fourier transform. Finally, a clustering algorithm is designed to obtain the clustering results of music data.
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THURAISINGHAM, BHAVANI. "MANAGING AND MINING MULTIMEDIA DATABASES." International Journal on Artificial Intelligence Tools 13, no. 03 (September 2004): 739–59. http://dx.doi.org/10.1142/s0218213004001776.
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Several advances have been made on managing multimedia databases as well as on data mining. Recently there is active research on mining multimedia databases. This paper provides an overview of managing multimedia databases and then describes issues on mining multimedia databases. In particular mining text, image, audio and video data are discussed.
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Wang, Fang. "The Effect of Multimedia Teaching Model of Music Course in Colleges and Universities Based on Classroom Audio Data Mining Technology." Tobacco Regulatory Science 7, no. 5 (September 30, 2021): 4520–31. http://dx.doi.org/10.18001/trs.7.5.2.18.
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Objectives: With the rapid development of information technology, multimedia teaching mode carries a large amount of audio-visual information, quickly occupies the music classroom in Colleges and universities, and becomes the mainstream teaching mode of music teaching in Colleges and universities. Methods: Based on this, this study uses classroom audio data mining technology to analyze the effect of multimedia teaching mode of music courses in Colleges and universities. The method of audio data mining is analyzed in college music multimedia classroom. The advanced embedded SOPC system is used to decode the MP3 audio files played in music courses by combining software and hardware. The performance of the multimedia teaching system in college music courses is optimized. Results: The hardware resources are made use of the flexibility of SOPC (System-on-a-Programmable-Chip) system. Reasonable allocation achieves the optimal design of teaching mode. Finally, the superiority of the algorithm is verified by testing. The test results show that the decoding speed and efficiency of audio files can be significantly improved by combining hardware and software. Conclusion: At the same time, the system has greater flexibility and expandable space, which can effectively promote the multimedia teaching effect of music courses in Colleges and universities. The research in this paper is helpful to the flexible transformation of multimedia teaching mode of music courses in Colleges and universities, and provides an important reference for the popularization of multimedia and the wide use of data mining technology in music courses in Colleges and universities.
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Paul, Prantosh K., and K. S. Shivraj. "Multimedia Data Mining and its Integration in Information Sector and Foundation: An Overview." Asian Journal of Computer Science and Technology 3, no. 1 (May 5, 2014): 24–28. http://dx.doi.org/10.51983/ajcst-2014.3.1.1729.
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Information and Communication Technologies are one of the important component and toll. Virtually, the advent of Electronic resources and similar foundation use in Information Foundation and similar foundation has brought about significant changes in storage and communication of information. Data mining process consist of several process and stages, which are related to each other and interactive. This is the way of mining or extraction of data from the Database or Dataset. Extraction of Data with multimedia nature such as audio, video, images, text may be called as Multimedia Data Mining. In Information Foundation, Data Mining has wonderful role and importance. This paper is talks about Multimedia Information and Data Mining and its characteristics. Paper also talks about role and need of Multimedia Data Mining in Information and Similar Foundation.
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Ye, Jiaxing, Takumi Kobayashi, Xiaoyan Wang, Hiroshi Tsuda, and Masahiro Murakawa. "Audio Data Mining for Anthropogenic Disaster Identification: An Automatic Taxonomy Approach." IEEE Transactions on Emerging Topics in Computing 8, no. 1 (January 1, 2020): 126–36. http://dx.doi.org/10.1109/tetc.2017.2700843.
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Li, Xaiomeng. "Construction of Teachers Performance Evaluation Index System for Data-Driven Smart Classrooms in Secondary Schools." SHS Web of Conferences 190 (2024): 03010. http://dx.doi.org/10.1051/shsconf/202419003010.
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Smart classroom is a new teaching paradigm for the digital transformation of education, which utilizes methods such as audio and video intelligent recognition, model construction, and data mining to evaluate teaching effectiveness and quality, in order to achieve automatic and full process evaluation and feedback of teacher teaching quality. This article is based on the massive real-time audio and video data generated by smart classrooms. By mining the hidden patterns and values of educational and teaching data, and using the Delphi method to construct a data-driven performance evaluation index system for secondary schools smart classroom teachers, it can fully reflect the real performance of secondary schools teachers in the smart classroom, achieving a comprehensive, all staff, fair, and objective evaluation of secondary schools teachers, overcoming the shortcomings of traditional evaluation methods.
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Shin, Sanghyun, Abhishek Vaidya, and Inseok Hwang. "Helicopter Cockpit Audio Data Analysis to Infer Flight State Information." Journal of the American Helicopter Society 65, no. 3 (July 1, 2020): 1–8. http://dx.doi.org/10.4050/jahs.65.032001.
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In recent years, the National Transportation Safety Board has highlighted the importance of analyzing flight data as one of the effective methods to improve the safety and efficiency of helicopter operations. Since cockpit audio data contain various sounds from engines, alarms, crew conversations, and other sources within a cockpit, analyzing cockpit audio data can help identify the causes of incidents and accidents. Among the various types of the sounds in cockpit audio data, this paper focuses on cockpit alarm and engine sounds as an object of analysis. This paper proposes cockpit audio analysis algorithms, which can detect types and occurrence times of alarm sounds for an abnormal flight and estimate engine-related flight parameters such as an engine torque. This is achieved by the following: for alarm sound analysis, finding the highest correlation with the short time Fourier transform, and the Cumulative Sum Control Chart (CUSUM) using a database of the characteristic features of the alarm; and for engine sound analysis, using data mining and statistical modeling techniques to identify specific frequencies associated with engine operations. The proposed algorithm is successfully applied to a set of simulated audio data, which were generated by the X-plane flight simulator, and real audio data, which were recorded by GoPro cameras in Sikorsky S-76 helicopters to demonstrate its desired performance.
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Faridzi, Salman Al, Faza Shafa Azizah, Faizal Mustafa, Azzahra Nindya Putri, Gilang Ramadhika, Fauzan Rizky Aditya, Ridha Sherli Fadilah, et al. "PENGOLAHAN DATA: PEMAHAMAN GEMPA BUMI DI INDONESIA MELALUI PENDEKATAN DATA MINING." Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS 2, no. 1 (February 16, 2024): 262–70. http://dx.doi.org/10.59407/jpki2.v2i1.506.
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Gempa bumi merupakan bencana alam yang sering terjadi di Indonesia akibat interaksi lempeng tektonik. Indonesia terletak pada pertemuan empat lempeng tektonik dunia, yang menyebabkan aktivitas zona tumbukan dan patahan yang berpotensi memicu gempa bumi. Meskipun telah terjadi sejumlah peristiwa gempa bumi besar di Indonesia, prediksi gempa secara tepat waktu masih sulit karena kompleksitas geologi dan dinamika kerak bumi. Peningkatan pemahaman tentang perilaku geologi dan sistem peringatan dini menjadi kunci dalam mempersiapkan diri menghadapi ancaman gempa bumi di masa mendatang. Data mining adalah proses yang berguna untuk mengeksplorasi dan mencari nilai informasi kompleks yang tersimpan dalam basis data. Dengan menggunakan data mining, dampak atau akibat dari gempa bumi yang terjadi di Indonesia dapat dipelajari berdasarkan data gempa bumi yang telah terjadi sebelumnya. Maka, dilakukanlah webinar dan workshop tentang penggunaan data mining untuk memahami pola gempa bumi di Indonesia selama 10 tahun terakhir. Webinar membahas dasar-dasar data mining dan fakta gempa yang terjadi di Indonesia, sementara workshop membahas pengolahan dan visualisasi data gempa bumi menggunakan bahasa Python dan Google Colab. Workshop ini terbatas pada pengolahan dan visualisasi data csv gempa bumi saja. Kegiatan webinar dan workshop dilaksanakan pada tanggal 29 Januari 2024 pukul 13.00 WIB. Hasil evaluasi menunjukkan bahwa peserta menyatakan kepuasan mereka terhadap acara tersebut, dengan sebagian besar peserta memberikan nilai positif terhadap penyampaian materi, kesesuaian materi dengan tema, kejelasan informasi, serta kualitas audio visual selama acara berlangsung.
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Bhoyar, Sanjay, Punam Bhoyar, Anuj Kumar, and Prabha Kiran. "Enhancing applications of surveillance through multimedia data mining." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 3 (2024): 1105–20. http://dx.doi.org/10.47974/jdmsc-1947.
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Over recent years, multimedia data has become a cornerstone for insightful data analysis, yielding vital information crucial for informed decision-making processes. This diverse data format encompasses audio, video, images, and text, offering a wealth of valuable knowledge. Advancements in multimedia acquisition, storage, and processing technologies have significantly enhanced analytical capabilities, overcoming challenges posed by semi-structured and unstructured data formats. Various entities including corporations, governmental bodies, and academic institutions are keenly interested in harnessing insights from the vast reservoirs of multimedia data generated across diverse sources. Consequently, researchers have delved into data mining methodologies, uncovering effective strategies for extracting insights from multimedia datasets. This study aims to probe the conceptual and practical dimensions of multimedia data mining within surveillance contexts, elucidating its transformative impact on diverse sectors by facilitating efficient data collection, analysis, and dissemination processes. Moreover, it underscores the significance of incorporating relevant cryptography methods to bolster the system’s integrity and completeness.
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Wang, Bo. "Multimedia Filtering Analysis of Massive Information Combined with Data Mining Algorithms." Advances in Multimedia 2021 (September 14, 2021): 1–7. http://dx.doi.org/10.1155/2021/7461874.
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With the advent of the big data era, information presentation has exploded. For example, rich methods such as audio and video have integrated more information, but with it, a lot of bad information has been brought. In view of this situation, this paper relies on data mining algorithms, builds a multimedia filtering system model for massive information, and integrates content recognition, packet filtering, and other technologies to match the two to ensure the integrity and real time of filtering. Practice results prove that the method is effective.
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Wang, Hua, Fuyu Zhu, and Zepeng Yao. "National culture learning platform based on big data mining." SHS Web of Conferences 187 (2024): 04009. http://dx.doi.org/10.1051/shsconf/202418704009.
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With the rapid development of big data and artificial intelligence technology, national culture learning platforms have become a new field of cultural communication and education. This article focuses on the development and application of the “National Culture Learning Platform Based on Big Data Mining” to explore how this platform can effectively spread national culture and promote the popularization of education. By integrating various data sources, such as documents, pictures, audio, and video, the platform can provide users with a comprehensive and in-depth national cultural learning experience. This article analyzes in detail the role of big data mining in content recommendation, personalized learning path design, and community interaction, proving that this new platform has important value in maintaining and disseminating national culture. Finally, the article also discusses the challenges and future development directions that the platform may face.
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Yu, Chen, Yiwen Zhong, Thomas Smith, Ikhyun Park, and Weixia Huang. "Visual Data Mining of Multimedia Data for Social and Behavioral Studies." Information Visualization 8, no. 1 (January 2009): 56–70. http://dx.doi.org/10.1057/ivs.2008.32.
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With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.
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TESEMA, Workineh. "INEFFICIENCY OF DATA MINING ALGORITHMS AND ITS ARCHITECTURE: WITH EMPHASIS TO THE SHORTCOMING OF DATA MINING ALGORITHMS ON THE OUTPUT OF THE RESEARCHES." Applied Computer Science 15, no. 3 (September 30, 2019): 73–86. http://dx.doi.org/10.35784/acs-2019-23.
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This review paper presents a shortcoming associated to data mining algorithm(s) classification, clustering, association and regression which are highly used as a tool in different research communities. Data mining researches has successfully handling large amounts of dataset to solve the problems. An increase in data sizes was brought a bottleneck on algorithms to retrieve hidden knowledge from a large volume of datasets. On the other hand, data mining algorithm(s) has been unable to analysis the same rate of growth. Data mining algorithm(s) must be efficient and visual architecture in order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams. The increasing use of information visualization tools (architecture) and data mining algorithm(s) stems from two separate lines of research. Data visualization researchers believe in the importance of giving users an overview and insight into the data distributions. Many powerful visual graphical interfaces are built on top of statistical analysis and data mining algorithms to permit users to leverage their power without a deep understanding of the underlying technology. The combination of the graphical interface is permit to navigate through the complexity of statistical and data mining techniques to create powerful models. Therefore, there is an increasing need to understand the bottlenecks associated with the data mining algorithms in modern architectures and research community. This review paper basically to guide and help the researchers specifically to identify the shortcoming of data mining techniques with domain area in solving a certain problems they will explore. It also shows the research areas particularly a multimedia (where data can be sequential, audio signal, video signal, spatio-temporal, temporal, time series etc) in which data mining algorithms not yet used.
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Gilbert, M., R. Moore, and G. Zweig. "Introduction to the Special Issue on Data Mining of Speech, Audio, and Dialog." IEEE Transactions on Speech and Audio Processing 13, no. 5 (September 2005): 633–34. http://dx.doi.org/10.1109/tsa.2005.852677.
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Gu Yueguo. "From real-life situated discourse to video-stream data-mining." International Journal of Corpus Linguistics 14, no. 4 (December 15, 2009): 433–66. http://dx.doi.org/10.1075/ijcl.14.4.01gu.
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This paper presents an argument for agent-oriented modeling (AOM) as a research methodology and a metalanguage for corpus linguistics. It is triggered by three closely related issues arising from compiling multimodal corpora such as the Spoken Chinese Corpora of Situated Discourse (SCCSD). Given a real-life situation, there are three types of representation: (i) the Written Word representation, (ii) audio recording, and (iii) video recording. It is shown that the three types are all data-transformative and involve data loss, and that they are intrinsically flawed. The current multiple-layered approach to data integration is also shown to be inadequate. AOM is proposed to be a potential solution to the problems. Modeling decision tree, levels of modeling, and modeling schema written in XML are demonstrated. The philosophical basis of AOM, and its theoretical implications are also discussed.
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Wang, Xin (Shane), Shijie Lu, X. I. Li, Mansur Khamitov, and Neil Bendle. "Audio Mining: The Role of Vocal Tone in Persuasion." Journal of Consumer Research 48, no. 2 (February 23, 2021): 189–211. http://dx.doi.org/10.1093/jcr/ucab012.
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Abstract Persuasion success is often related to hard-to-measure characteristics, such as the way the persuader speaks. To examine how vocal tones impact persuasion in an online appeal, this research measures persuaders’ vocal tones in Kickstarter video pitches using novel audio mining technology. Connecting vocal tone dimensions with real-world funding outcomes offers insight into the impact of vocal tones on receivers’ actions. The core hypothesis of this paper is that a successful persuasion attempt is associated with vocal tones denoting (1) focus, (2) low stress, and (3) stable emotions. These three vocal tone dimensions—which are in line with the stereotype content model—matter because they allow receivers to make inferences about a persuader’s competence. The hypotheses are tested with a large-scale empirical study using Kickstarter data, which is then replicated in a different category. In addition, two controlled experiments provide evidence that perceptions of competence mediate the impact of the three vocal tones on persuasion attempt success. The results identify key indicators of persuasion attempt success and suggest a greater role for audio mining in academic consumer research.
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Zvyagin, Leonid S. "MODERN TOOLS FOR DATA ANALYSIS AND WORK WITH NUMERICAL INFORMATION IN THE ECONOMY OF MODERN ENTERPRISES." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 1/1, no. 133 (2023): 166–73. http://dx.doi.org/10.36871/ek.up.p.r.2023.01.01.016.
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As a result of the development of information technologies, the amount of data accumulated in electronic form is growing rapidly. This data exists in various formats: texts, images, audio, video, hypertext documents, relational databases, and so on. However, the vast majority of the available information does not bring any benefit to a particular person, since he is not able to process such a large amount of information. There is a problem of extracting useful information for the user from a large amount of data. This circumstance gave rise to such technology as data Mining. Now Data Mining is one of the actively developing areas of information technology designed to identify useful knowledge from databases of various nature.
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Hajiaghajani, Azam. "Concepts and applications of data mining and analysis of social networks." Journal of Data Analytics 2, no. 1 (April 22, 2023): 1–8. http://dx.doi.org/10.59615/jda.2.1.1.
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Social media has become an important reference for information during the last few decades. They have been able to be effective in various fields such as business, entertainment, science, crisis management, politics, etc. For this reason, a social media analysis has become very important for researchers and large companies. The widespread use of social media leads to a complex problem called "accumulation of data". Many data science specialists seek to analyze this data in order to identify the behavioral characteristics of users, analyze interests and needs, and improve marketing processes. Different social media platforms have the ability to use all kinds of media, including text data, video, video, audio, and location information, etc. Therefore, data analysis in social networks is very important. In this research, the concepts and applications of data analysis in social networks will be investigated.
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Liu, Zhenguang, Sihao Hu, Yifang Yin, Jianhai Chen, Kevin Chiew, Luming Zhang, and Zetian Wu. "Interactive Rare-Category-of-Interest Mining from Large Datasets." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4965–72. http://dx.doi.org/10.1609/aaai.v34i04.5935.
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In the era of big data, rare category data examples are often of key importance despite their scarcity, e.g., rare bird audio is usually more valuable than common bird audio. However, existing efforts on rare category mining consider only the statistical characteristics of rare category data examples, while ignoring their ‘true’ interestingness to the user. Moreover, current approaches are unable to support real-time user interactions due to their prohibitive computational costs for answering a single user query.In this paper, we contribute a new model named IRim, which can interactively mine rare category data examples of interest over large datasets. The mining process is carried out by two steps, namely rare category detection (RCD) followed by rare category exploration (RCE). In RCD, by introducing an offline phase and high-level knowledge abstractions, IRim reduces the time complexity of answering a user query from quadratic to logarithmic. In RCE, by proposing a collaborative-reconstruction based approach, we are able to explicitly encode both user preference and rare category characteristics. Extensive experiments on five diverse real-world datasets show that our method achieves the response time in seconds for user interactions, and outperforms state-of-the-art competitors significantly in accuracy and number of queries. As a side contribution, we construct and release two benchmark datasets which to our knowledge are the first public datasets tailored for rare category mining task.
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Siddiquee, Md Mahfuzur Rahman, Md Saifur Rahman, Shahnewaz Ul Islam Chowdhury, and Rashedur M. Rahman. "Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation." International Journal of Software Innovation 4, no. 2 (April 2016): 71–87. http://dx.doi.org/10.4018/ijsi.2016040105.
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In this research, the authors propose an intelligent system that can recommend songs to user according to his choice. They predict the next song a user might prefer to listen based on their previous listening patterns, currently played songs and similar music based on music data. To calculate music similarity the authors used a Matlab toolbox that considers audio signals. They used association rule mining to find users' listening patterns and predict the next song the user might prefer. As they propose a music discovery service as well, the authors use the information of music listening pattern and music data similarity to recommend a new song. Later in result section, they replaced the audio based similarity with last.fm api for similar song listing and analyzed the behaviour of their system with the new list of songs.
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Li, Juan. "Application of Intelligent Archives Management Based on Data Mining in Hospital Archives Management." Journal of Electrical and Computer Engineering 2022 (April 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/6217328.
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Data mining belongs to knowledge discovery, which is the process of revealing implicit, unknown, and valuable information from a large amount of fuzzy application data. The potential information revealed by data mining can help decision makers adjust market strategies and reduce market risks. The information excavated must be real and not universally known, and it can be the discovery of a specific problem. Data mining algorithms mainly include the neural network method, decision tree method, genetic algorithm, rough set method, fuzzy set method, association rule method, and so on. Archives management, also known as archive work, is the general term for various business works, in which archives directly manage archive entities and archive information and provide utilization services. It is also the most basic part of national archives. Hospital archives are an important part of hospital management, and hospital archives are the accumulation of work experience and one of the important elements for building a modern hospital. Hospital archives are documents, work records, charts, audio recordings, videos, photos, and other types of documents, audio-visual materials, and physical materials, such as certificates, trophies, and medals obtained by hospitals, departments, and individuals. The purpose of this paper is to study the application of intelligent archives management based on data mining in hospital archives management, expecting to use the existing data mining technology to improve the current hospital archives management. This paper investigates the age and educational background of hospital archives management workers and explores the relationship between them and the quality of archives management. Based on the decision number algorithm, on the basis of the database, the hospital data is classified and analyzed, and the hospital file data is classified and processed through the decision number algorithm to improve the system data processing capability. The experimental results of this paper show that among the staff working in the archives management department of the hospital, 20-to-30-year-olds account for 46.2% of the total group. According to the data, the staff in the archives management department of the hospital also tends to be younger. Among the staff under the age of 30, the file pass rate was 98.3% and the failure rate was 1.7%. Among the staff over 50 years old, the file pass rate was 99.9% and the failure rate was 0.1%. According to the data, the job is related to the experience of the employee.
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Huang, Chunyuan. "Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/4572904.
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In the process of vocal music learning, incorrect vocalization methods and excessive use of voice have brought many problems to the voice and accumulated a lot of inflammation, so that the level of vocal music learning stagnated or even declined. How to find a way to improve yourself without damaging your voice has become a problem that we have been pursuing. Therefore, it is of great practical significance for vocal music teaching in normal universities to conduct in-depth research and discussion on “pharyngeal singing.” Based on audio extraction, this paper studies the vocal music teaching pharyngeal training method. Different methods of vocal music teaching pharyngeal training have different times. When the recognition amount is 3, the average recognition time of vocal music teaching pharyngeal training based on data mining is 0.010 seconds, the average recognition time of vocal music teaching pharyngeal training based on Internet of Things is 0.011 seconds, and the average recognition time of vocal music teaching pharyngeal training based on audio extraction is 0.006 seconds. The recognition time of the audio extraction method is much shorter than that of the other two traditional methods, because the audio extraction method can perform segmented training according to the changing trend of physical characteristics of notes, effectively extract the characteristics of vocal music teaching pharyngeal training, and shorten the recognition time. The learning of “pharyngeal singing” in vocal music teaching based on audio extraction is different from general vocal music training. It has its unique theory, concept, law, and sound image. In order to “liberate your voice,” it adopts large-capacity and large-scale training methods.
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Abimbola Owonipa, Ayodeji, Taye Oladele Aro, and Oyenike Adunni Olukiran. "Multimedia Data Mining and Processing for News Source Attribution." International Journal of Research and Review 11, no. 5 (May 10, 2024): 48–59. http://dx.doi.org/10.52403/ijrr.20240507.
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The desire for unbiased journalism that effectively counters disinformation is widely recognised. News consumers are not only interested in news, but they also want unbiased journalism that cuts through disinformation, and they want it from trusted news sources. Consequently, media researchers need to explore ways to facilitate news-source identification, irrespective of the platform used. However, the availability of multimedia data sources has seen a remarkable surge in recent years, encompassing demographic data, social media data, geodata, and pervasive digital trace data. Multimedia data mining is a procedure of discovering stimulating trends via media data using video, text, and audio that are not generally available by simple enquiries and related outputs. Researchers face the challenge of integrating these diverse sources to enhance news source attribution in multimedia data including platforms like Facebook, WhatsApp and Instagram. The paper presents a review of multimedia data approaches and their application to news source attribution research. Also, the examination of the benefits and limitations of these techniques and discussion on future directions were mentioned. Consideration was on machine learning and statistical approaches to multimedia data, which include deep learning, and probabilistic modelling. Similarly, a discussion on the importance of data privacy and ethics in news source attribution research was stated. The contribution of this study is highly relevant for news media research groups striving to improve their capability to attribute sources in multimedia data, thereby combatting disinformation and amplifying trusted media brands. Keywords: Data mining, Multimedia, Data process, News source attribution, Unbiased journalism
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La, Lei, Shuyan Cao, and Liangjuan Qin. "Take full advantage of unlabeled data for sentiment classification." Kybernetes 47, no. 3 (March 5, 2018): 474–86. http://dx.doi.org/10.1108/k-08-2016-0196.
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Purpose As a foundational issue of social mining, sentiment classification suffered from a lack of unlabeled data. To enhance accuracy of classification with few labeled data, many semi-supervised algorithms had been proposed. These algorithms improved the classification performance when the labeled data are insufficient. However, precision and efficiency are difficult to be ensured at the same time in many semi-supervised methods. This paper aims to present a novel method for using unlabeled data in a more accurate and more efficient way. Design/methodology/approach First, the authors designed a boosting-based method for unlabeled data selection. The improved boosting-based method can choose unlabeled data which have the same distribution with the labeled data. The authors then proposed a novel strategy which can combine weak classifiers into strong classifiers that are more rational. Finally, a semi-supervised sentiment classification algorithm is given. Findings Experimental results demonstrate that the novel algorithm can achieve really high accuracy with low time consumption. It is helpful for achieving high-performance social network-related applications. Research limitations/implications The novel method needs a small labeled data set for semi-supervised learning. Maybe someday the authors can improve it to an unsupervised method. Practical implications The mentioned method can be used in text mining, image classification, audio processing and so on, and also in an unstructured data mining-related field. Overcome the problem of insufficient labeled data and achieve high precision using fewer computational time. Social implications Sentiment mining has wide applications in public opinion management, public security, market analysis, social network and related fields. Sentiment classification is the basis of sentiment mining. Originality/value According to what the authors have been informed, it is the first time transfer learning be introduced to AdaBoost for semi-supervised learning. Moreover, the improved AdaBoost uses a totally new mechanism for weighting.
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Deng, San Peng, Xin Yong Wang, Yu Ming Qi, Chun Ming Wang, and Kang Sun. "Design of Intelligent Transmission System for Mining Belt-Conveyor Group." Applied Mechanics and Materials 496-500 (January 2014): 1494–97. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1494.
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£oBased on PLC, ARM and CAN bus technology, the intelligent transmission system is invented, which can be used to transfer the safety production monitoring data and audio data of belt conveyor group. It is able to effectively solve the problems in transmitting voice information clearly, collecting and managing multi-sensor information over dozens of kilometers along the transport fleet about the conveyor group monitoring systems. Its application in industry field shows that it effectively raises the belt conveyor system level of remote intelligent monitoring and emergency safety integrated protection.
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Rajadnya, Vibhavari, and Dr Kalyani Joshi. "Raga Classification Based on Novel Method of Pitch Co-Occurrence." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 1 (May 30, 2022): 23–27. http://dx.doi.org/10.35940/ijrte.a6886.0511122.
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Automatic identification of raga is a growing research area and has captured significant attention from movie making industry. It is the need of time to develop efficient tools for data mining the vast audio visual data on internet. In particular, to search for a specific raga. Applications of raga search are in musicological studies, similarity based search. Ascending and descending pattern of swaras is an important feature in the raga classification. Pitch tracks of swaras are obtained from raw audio recordings. This research has utilised the pattern developed due to co-occurrence of pitches of swaras for classification. This pattern gives a concise representation of the signal which contains time and frequency information of the raga. K Nearest Neighbour (KNN) has been used as the classifier.
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Lin, Dingbao. "Application of a Big Data Platform in the Course of Java Language Programming." International Journal of Emerging Technologies in Learning (iJET) 11, no. 10 (October 27, 2016): 16. http://dx.doi.org/10.3991/ijet.v11i10.6264.
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This study designs a big data teaching platform that adopts the big data technology of Hadoop as its core. The platform provides a method of searching teaching video data and integrates the technologies of data encryption and intelligent recognition. The teaching functions of this advanced platform include teaching audio–visual presentation, intelligent data recognition, data-mining sharing, and data storage sharing. The method of comparative teaching is adopted to test the actual effect of the teaching platform on teaching Java language programming to students majoring in computer. Results show that the platform exhibits good teaching applicability, significantly improves the examination achievements, learning interest, independent learning ability, and innovation ability of students, and therefore indicates a promising application prospect and research value.
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Jia, Xiaosong. "A Music Emotion Classification Model Based on the Improved Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (February 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/6749622.
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Aiming at the problems of music emotion classification, a music emotion recognition method based on the convolutional neural network is proposed. First, the mel-frequency cepstral coefficient (MFCC) and residual phase (RP) are weighted and combined to extract the audio low-level features of music, so as to improve the efficiency of data mining. Then, the spectrogram is input into the convolutional recurrent neural network (CRNN) to extract the time-domain features, frequency-domain features, and sequence features of audio. At the same time, the low-level features of audio are input into the bidirectional long short-term memory (Bi-LSTM) network to further obtain the sequence information of audio features. Finally, the two parts of features are fused and input into the softmax classification function with the center loss function to achieve the recognition of four music emotions. The experimental results based on the emotion music dataset show that the recognition accuracy of the proposed method is 92.06%, and the value of the loss function is about 0.98, both of which are better than other methods. The proposed method provides a new feasible idea for the development of music emotion recognition.
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Devaraj, Saravanan. "Video data image retrieval using – BRICH." World Journal of Engineering 14, no. 4 (August 7, 2017): 318–23. http://dx.doi.org/10.1108/wje-09-2016-0093.
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Purpose Data mining is the process of detecting knowledge from a given huge data set. Among the data set, multimedia is the data which contains diverse data such as audio, video, image, text and motion. In this growing field of video data, mining the video data plays vital role in the field of video data mining. In video data mining, video data are grouped into frames. In this vast amount of video frames, the fast retrieval of needed information is important one. This paper aims to propose a Birch-based clustering method for content-based image retrieval. Design/methodology/approach In image retrieval system, image segmentation plays a very important role. A text file, normally, is divided into sections, that is, piece, sentences, word and character for this information which are organized and indexed effectively like in a video, the information is dynamic in nature and this information is converted to static for easy retrieval. For this, video files are divided into a number of frames or segments. After the segmentation process, images are trained for retrieval process, and from these, unwanted images are removed from the data set. The noise or unwanted image removal pseudo-code is shown below. In the code image, pixel value represents the value of the difference between the two adjacent image pixel values. By assuming a threshold for the image value, the duplicate images are found. After finding the duplicate image, it is removed from the data set. Clustering is used in many applications as a stand-alone tool to get insight into data distribution and as a pre-processing step for other algorithms (Ester et al., 1996). Specifically, it is used in pattern recognition, spatial data analysis, image processing, economic science document classification, etc. Hierarchical clustering algorithms are classified as agglomerative or divisive. BRICH uses clustering attribute (CA) and clustering feature hierarchy (CA_Hierarchy) for the formation of clusters. It perform multidimensional data objects. Every BRICH algorithm based on the memory-oriented information, that is, memory constrains, is involved in the processing of the data sets. This information is represented in Figures 6-10. For forming clusters, they use the amount of object in the cluster (A), the sum of all points in the data set (S) and need the square value of the all objects (P). Findings The proposed technique brings an effective result for cluster formation. Originality/value BRICH uses a novel approach to model the degree of inter-connectivity and closeness between each pair of clusters that takes into account the internal characteristics of the clusters themselves.
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Mane, Deepak, Dr Sirbi Kotrappa, and Kiran Shibe. "Sentiment Analytics on Chinese Product Boycott from Multiple Data Sources." Computational Intelligence and Machine Learning 2, no. 1 (April 20, 2021): 16–25. http://dx.doi.org/10.36647/ciml/02.01.a003.
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Sentiment Analysis and Opinion mining is a technique recognizing and drawing out the personalized information underlying a different kind of documents such as text, audio, images and videos. This area of research tries to exaplain the feeling, opinions, emotions of people on something topics. The most relevant classifying a statement as ‘positive’ , ‘negative’ and ‘neutral’ from records/posts obtained from different source system such as Twitter, Facebook , Reddit etc. To predict the sentiment/result of recent Chinese Product Boycott campaign, This paper direct to operate on data received from 9 different sources. In the field of Trade and commerce where traders. Politians and Peoples need to catch public’s point of view, thinking and therefor evaluate people’s reaction about Chinese product. The reasoning behind performing this research is that, the prediction will also help to know what is reason behind this , Chinese product boycott analysis will have a major impact on relationship between India and China trade. Keyword : Sentiment, Chinese Product, Data Sources, Boycott
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Sotirova-Valkova, Kalina. "A pilot conceptualisation of the data space ecosystems for cultural heritage." Mathematics and Education in Mathematics 53 (March 16, 2024): 92–98. http://dx.doi.org/10.55630/mem.2024.53.092-098.
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The digital heritage sector is among the nine common European data spaces out- lined in the EU Data Act: Health, Industrial, Agriculture, Finance, Mobility, Green Deal, Energy, Public Administration, and Skills. The data space concept is complex, and being linked with the data ecosystem brings together methodologies and techniques from numerous domains: artificial intelligence (AI), text/data mining, data visualisation, mapping, image analysis, audio analysis, network analysis, and rights management. There is a need for clarifying data terminology in use for the GLAM field and transferable methodology to strengthen its’ ongoing datafication and digital skill set. The paper offers a comprehensive literature review (incl. EU Data (space) policy documents) on data spaces and data ecosystems aimed at (1) formulating the main challenges in data collection, data processing, and data maturity for heritage-related information systems and (2) justification of the necessary mindset and digital skill set change for a mature museum. General conclusions are made as well as rec- ommendations for the Bulgarian GLAM sector.
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Abdul bin Ismail. "Nonnegative Matrix Factorization: A Review." December 2023 2, no. 2 (September 2023): 324–42. http://dx.doi.org/10.36548/rrrj.2023.2.006.
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Recent developments in Non-negative Matrix Factorization (NMF) have focused on addressing several challenges and advancing its applicability. New algorithmic variations, such as robust NMF, deep NMF, and graph-regularized NMF, have emerged to improve NMF's performance in various domains. These developments aim to enhance the interpretability, scalability, and robustness of NMF-based solutions. NMF is now widely used in audio source separation, text mining, recommendation systems, and image processing. However, NMF still faces challenges, including sensitivity to initialization, the determination of the appropriate rank, and computational complexity. Overlapping sources in audio and data sparsity in some applications remain challenging issues. Additionally, ensuring the consistency and stability of NMF results in noisy environments is a subject of ongoing research. The quest for more efficient and scalable NMF algorithms continues, especially for handling large datasets. While NMF has made significant strides in recent years, addressing these challenges is crucial for unlocking its full potential in diverse data analysis and source separation tasks.
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Tanweer, Safdar, and Naseem Rao. "Novel Algorithm of CPU-GPU hybrid system for health care data classification." Journal of Drug Delivery and Therapeutics 9, no. 1-s (February 21, 2019): 355–57. http://dx.doi.org/10.22270/jddt.v9i1-s.2445.
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Due to advancements in portable health monitoring technology, such systems have become more and more economical & efficient. This in turn has resulted in a huge amount of data being generated every moment by millions of users of such portable devices. Such voluminous data may include audio, video, and image, and text representing blood pressure, temperature, vocal activity, ECG, sugar level etc. In the Proposed algorithm, first step is assignment, where clusters are assigned to a patient data and the second step is update, which takes the mean of the coordinates of all the data in its cluster. Medical practitioners and service providers can use such data to discover various patterns and useful insights. Such insights can be very useful on understanding various trends during epidemics, such as Malaria, Dengue, Chikungunya and other such outbreaks. A faster and economical way to get such insights is of paramount importance.
Keywords: health monitoring; GPU; ECG; epidemics; data mining
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Rao, Naseem, and Safdar Tanweer. "Performance Analysis of Healthcare data and its Implementation on NVIDIA GPU using CUDA-C." Journal of Drug Delivery and Therapeutics 9, no. 1-s (February 21, 2019): 361–63. http://dx.doi.org/10.22270/jddt.v9i1-s.2447.
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In this paper we show how commodity GPU based data mining can help classify various healthcare data in different groups faster than traditional CPU based systems. In addition such systems are cheaper than various ASIC (Application Specific Integrated Circuits) based solutions. Such faster clustering of data could provide useful insights for making successful decisions in case of emergency and outbreaks. Finally, we present conclusion based on our research done so far. In our work we used NVIDIA GPU for implementing an algorithm for healthcare data classification. Speech dissiliency and stuttering assessment can also be addressed through classification audio/speech samples using ANN, k-NN, SVM etc4. Such a faster and economical way to get such insights is of paramount importance. Specifically as a proof-of-concept we have implement k-means algorithm on health care related data set.
Keywords: NVIDIA; GPU; ECG; CPU; ANN.
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Zhang, Jianmin, Zhaofa Zeng, Xueyu Zhao, Jing Li, Yue Zhou, and Mingxu Gong. "Deep Mineral Exploration of the Jinchuan Cu–Ni Sulfide Deposit Based on Aeromagnetic, Gravity, and CSAMT Methods." Minerals 10, no. 2 (February 13, 2020): 168. http://dx.doi.org/10.3390/min10020168.
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The exploration of deep mineral resources is an important prerequisite for meeting the continuous demand of resources. The geophysical method is one of the most effective means of exploring the deep mineral resources with a large depth and a high resolution. Based on the study of the geological background, petrophysical properties, and aeromagnetic anomaly characteristics of the Jinchuan Cu–Ni sulfide deposit, which is famous throughout the world, this paper uses the widely used gravity, aeromagnetic, and CSAMT (controlled source audio-frequency magnetotellurics) methods with a complementary resolution to reveal the favorable prospecting position. In order to obtain better inversion results, the SL0 norm tight support focusing regularization inversion method is introduced to process the section gravity and aeromagnetic data of the mining area. By combining the results with CSAMT, it is found that the medium-low resistivity, high density, and the high magnetic anomaly areas near the structural belt can nicely correspond with the known ore-bearing rock masses in the mining area. At the same time, according to the geophysical exploration model and geological and physical property data, four favorable ore-forming prospect areas are delineated in the deep part of the known mining area.
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Bhat, Prashant, and Pradnya Malaganve. "Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database." International Journal of Intelligent Systems and Applications 13, no. 4 (August 8, 2021): 38–48. http://dx.doi.org/10.5815/ijisa.2021.04.04.
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Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.
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Abumalloh, Rabab Ali, Mahmud Alrahhal, Nahla El-Haggar, Albandari Alsumayt, Zeyad M. Alfawaer, and Sumayh S. Aljameel. "Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study." Emerging Science Journal 8, no. 1 (February 1, 2024): 140–52. http://dx.doi.org/10.28991/esj-2024-08-01-010.
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Cyber-attacks have become increasingly prevalent with the widespread integration of technology into various aspects of our lives. The surge in social media platform usage has prompted users to share their firsthand experiences with cyber-attacks. Despite this, previous literature has not extensively investigated individuals' experiences with these attacks. This study aims to comprehensively explore and analyze the content shared by cyber-attack victims in Saudi Arabia, encompassing text, video, and audio formats. The primary objective is to investigate the factors influencing victims' perceptions of the security risks associated with these attacks. Following data collection, preparation, and cleaning, Latent Dirichlet Allocation (LDA) is employed for topic modeling, shedding light on potential factors impacting victims. Sentiment analysis is then utilized to examine the nuanced negative and positive perceptions of individuals. NVivo is deployed for data inspection, facilitating the presentation of insightful inferences. Hierarchical clustering is implemented to explore distinct clusters within the textual dataset. The study's results underscore the critical importance of spreading awareness among individuals regarding the various tactics employed by cyber attackers. Doi: 10.28991/ESJ-2024-08-01-010 Full Text: PDF
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Liu, Jingfang, and Lu Gao. "Are Diverse Media Better than a Single Medium? The Relationship between Mixed Media and Perceived Effect from the Perspective of Online Psychological Counseling." International Journal of Environmental Research and Public Health 18, no. 16 (August 14, 2021): 8603. http://dx.doi.org/10.3390/ijerph18168603.
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The progress of new media has promoted the development of online health consultations. Previous research has investigated the impact of media richness on user satisfaction; however, little attention has been given to the mixed effects of the nesting of multiple media. The purpose of this study is to analyze the impact and differences of the use of single or mixed media on users’ perceived effect from the perspectives of social support and satisfaction by mining user reviews on online health platforms. The data were collected from a professional online psychological counseling platform. We collected data on 48,807 reviews from 11,694 users. Text annotation and sentiment analysis were then used to extract variable eigenvalues from the reviews. One-way analysis of variance (ANOVA) and hierarchical regression analysis were used for statistical analysis. The results show that mixed media with different richness has a significant impact on the users’ perceived effects. Among them, compared to “text + audio,” using “text + audio + video/face to face” can significantly improve the users’ perceived social support and satisfaction. However, compared to single medium, mixed media with higher richness may not necessarily achieve a better effect. We found that the inclusion of “video/face to face” mixed media significantly reduced the users’ perceived social support and satisfaction compared to text or audio use alone. These research results complement the blank media richness theory in the field of online health care and provide guidance for improving the personalized customization of online psychological counseling platforms.
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Naveed, Muhammad Asif, and Asif Ali. "Health and Safety Information Behaviour of Coal Miners in Pakistan." Libri 71, no. 1 (January 21, 2021): 29–40. http://dx.doi.org/10.1515/libri-2019-0132.
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AbstractThis research investigated health and safety information behaviour of miners working at Makarwal Coal Mines, district Mianwali, Punjab, Pakistan. A survey method using a questionnaire was deployed for data collection from 136 coal workers. Descriptive statistics were applied for data analysis using SPSS. The results indicated that the miners’ information needs were cantered mainly on protection equipment, mining diseases, healthcare services, geological hazards, accidents associated with mining and modern mining techniques. These miners relied overwhelmingly on interpersonal relationships with fellow miners and friends for safety information followed by television and radio. A good number of the survey participants also utilized internet and social media such as Facebook, WhatsApp, etc. as information source. There was little evidence of the use of audio/visual materials, seminar/workshops, associations, government agencies, and printed materials for information acquisition. Computer illiteracy, poor financial conditions, lack of time, language barriers, lack of awareness and knowledge about safety information and non-availability of relevant materials were perceived as the major constraints in acquiring safety information. The results will not only be useful for planning a need-based information infrastructure for miners but also for policymakers, NGOs and human rights organizations working for rural development and uplifting occupational health. This research contributed in the existing research on miners’ information behaviour as only a few studies appeared.
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Huang, Xiaoyu, and Svetlana V. Bakuto. "Song Emotion Intelligence Analysis for Psychological Stress Relief." International Journal of Information Systems and Supply Chain Management 17, no. 1 (February 19, 2024): 1–21. http://dx.doi.org/10.4018/ijisscm.338719.
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In today's digital and networked era, multimedia content such as images, audio, and video have become an important part of the data transmitted on the Internet's information superhighway. How to manage the stress in the college student population is directly related to the future life and development of college students. In this article, based on domestic and international data mining technology, the authors designed an intelligent analysis system for college students' mental health, pre-processed the data, then analysed these data in detail by using the outlier analysis algorithm in clustering algorithm, and finally mined the intrinsic connection between the psychological problems and attributes by using the Apriori association rule algorithm, so as to provide the decision makers with a reliable basis. This study puts forward reasonable solutions and suggestions for the problems existing in the psychological level of contemporary college students.
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Zhang, Shao Hua, and Yan Qing Wu. "Research on the Fast Network Rollout Performance of Mine Multimedia Emergency Communication System Based on WMN." Advanced Materials Research 271-273 (July 2011): 1103–7. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1103.
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A Multimedia Emergency Communication System based on Wireless Mesh Network in Coal Mine(MECS-WMN) is proposed. Audio, video, environmental data (methane, oxygen, temperature, carbon monoxide, hydrogen sulfide) and rescue personnel position information of Disaster Site, which captured by mining multimedia Terminals are transmitted through the Wireless Mesh Network to Mine Rescue Base and Ground Rescue Center in real-time. The program of using current wireless AP signal strength determines the next AP laying location to solve the problem "Fast Network Rollout, Plug and Play" of MECS-WMN. The experiment results show that the program truly implements real-time communications between rescue workers and command bases, which plays an important role in emergency communication and mine safety.
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Polyakova, A. S., and L. V. Lipinskiy. "Decision Rule Ensemble Formation Via a Multicriteria Evolutionary Algorithm for the Problem of Human Emotion Analysis in Audio Data." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 4 (127) (August 2019): 45–61. http://dx.doi.org/10.18698/0236-3933-2019-4-45-61.
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One of the most important problems at the current stage of social informatisation is development of human-machine interaction systems, including automated human emotion recognition systems. It is possible to describe human emotions using a combination of two parameters: Valence, which represents how attractive an emotion is (referring to positive and negative emotions), and Arousal, denoting the strength of the emotion (that is, degree of agitation). These parameters are real numbers. We propose to employ ensemble learning methods to improve prediction accuracy. We evaluate the accuracy of an ensemble decision via its congruence coefficient. We used a multicriteria evolutionary algorithm to select agents (algorithms) for the ensemble. Employing a multicriteria evolutionary algorithm made it possible to automate the ensemble formation process, which enabled us to save time and physical resources. Ensemble formation depended on two criteria: maximising accuracy and minimising the number of agents in the ensemble. We used the following ensemble decision-making methods: majority voting, weighted average, weighted average in proportion to the agent trust, and a fuzzy logic system. We present a modification to the fuzzy logic system that improves solution efficiency for the data mining problem. We analysed and investigated how efficient a multicriteria evolutionary algorithm is when solving the problem of predicting emotional behaviour in humans. Our experiments showed that using a multicriteria evolutionary algorithm to automate ensemble formation improves the solution accuracy.
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Karthikeyan, D., Arumbu V. P., K. Surendhirababu, K. Selvakumar, P. Divya, P. Suhasini, and R. Palanisamy. "Sophisticated and modernized library running system with OCR algorithm using IoT." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (December 1, 2021): 1680. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1680-1691.
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An internet of things (IoT) is an exclusive method, were its impact on the enactments of human life is very trendy. This research on library control system operates on the basis of IoT and optical character recognition (OCR) algorithm rules and its training. A closed-circuit television (CCTV) watched mechanism is created to control the book issuing and returning phenomenon via tag studying system in the library. In this proposed work text file is converted into an audio file. This audio file is being played and the contents of the book can be heard via the headset. This unique function of the OCR helps blind people. Now a days OCR widely focused in machine processes such as machine transformation, text to speech extraction and text data mining. It utilized in various area of research in artificial intelligence, computer vision and pattern recognition. Using OCR to scan the damaged book in the library converted into pdf format the book gets new life and sharing the contents to multiple readers. In this paper aims to implement IoT based library management system to maintaining books in digital format.
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Hamzah, Islaq Hastita, Tuti Bahfiarti, and M. Iqbal Sultan. "PERSONAL BRANDING PT. VALE INDONESIA DI INSTAGRAM." Al-KALAM : JURNAL KOMUNIKASI, BISNIS DAN MANAJEMEN 10, no. 2 (July 12, 2023): 88. http://dx.doi.org/10.31602/al-kalam.v10i2.10650.
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Personal branding by a company is important to build a positive image for itsaudiences. One of the social media that is widely used is Instagram because it can bringup two-way communication and audio-visual sharing. PT. Vale Indonesia Tbk., a nickelmining company chose Instagram to build its personal branding as a way of marketingitself and to be remembered by the audience as a mining company that has excellencewith professional goals.This research is qualitative research using non-participant observation data collectiontechniques and documentation by looking at the content uploaded, content in the formof images and captions.The results of the study show that PT. Vale Indonesia builds personal branding as amining company that cares for fellow human beings and sustainable nature, supportslocal MSMEs, and upholds gender equality.
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Long, Jianhui, Jin Liu, Sheng Zhang, and Meiping Li. "Comprehensive Evaluation of Goaf Range in a Coal Mine with a Complex Terrain through CSAMT and an Activated-Carbon Method for Radon Measurement." Applied Sciences 13, no. 7 (March 28, 2023): 4274. http://dx.doi.org/10.3390/app13074274.
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Underground goaves were left in many mining areas due to the continuous exploitation of coal resources. These mining areas seriously affect the production safety of the mines and the safety of life and property of the surrounding residents. Enormous safety hazards will be generated if the goaf range is not accurately controlled. In this study, we proposed a method for the detection of goaves in coal mines with a complex terrain by combining controlled source audio-frequency magnetotellurics (CSAMT) and an activated-carbon method for radon measurement. The disadvantage of failing to interpret goaf depth for the activated-carbon method for radon measurement was compensated by the advantage of the capability of goaf-depth sounding for CSAMT. Subsequently, the reference for CSAMT data was provided by the immunity of the activated-carbon method for radon measurement to the influences of terrain, earth electricity, and EMF. On this basis, the proposed method was employed to detect the goaf of Houjiagou Coal Mine in Liulin County, China, and obtained reliable detection results. The feasibility of the comprehensive geophysical prospecting method in the complex terrain was verified and it provides a new reference for the detection method of goaves with other conditions.
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Xu, Yi, and Feng Zhou. "Design of the Higher Education System Based on Parallel Association Rules Algorithm." Wireless Communications and Mobile Computing 2022 (March 1, 2022): 1–11. http://dx.doi.org/10.1155/2022/8602545.
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Aiming at improving the quality of online education, a higher education system based on parallel association rules algorithm is designed. In this study, the functional structure module of the system is divided into six modules, namely, the home page module, course module, teacher module, student module, administrator module, and personal center, so as to carry out a comprehensive treatment for students, teaching, and education resources. On the basis of mining the data association rules of the education system, the parallel association rules algorithm is used to identify and analyze the original data of the system, so as to fundamentally improve the ability of the system to process data and complete the personalized recommendation of educational resources and teaching evaluation feedback. Experiments show that the design system has greater information throughput, a short response time, and a resource utilization rate of more than 90%. In addition, the audio teaching resource plays better, which proves that the system effectively achieves the design expectation.
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Fadhli, Muhibuddin, Dedi Kuswandi, Prihma Sinta Utami, Septi Budi Sartika, and Mohamad Hardyman bin Barawi. "Game-Based Learning and Children’s Digital Literacy to Support Pervasive Learning: A Systematic Reviews." JTP - Jurnal Teknologi Pendidikan 25, no. 3 (December 27, 2023): 386–93. http://dx.doi.org/10.21009/jtp.v25i3.38388.
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In this study, we investigate the impact of game-based learning on children's digital literacy in processing and acquiring information, focusing on the generation of digital natives who excel in responding to multimedia information. Utilizing data mining and the PRISMA Protocol, we conducted a systematic review based on the keywords 'Game-based learning on children’s digital literacy.' Data from the years 2017 to 2019, extracted from Sagepub and Emerald databases, reveal strong empirical support for the enhancement of children's digital literacy, particularly through game-based learning interventions, as evidenced by a significant effect size of 0.66. This translates to children becoming better at deciphering visual cues, understanding audio narratives, and critically evaluating the information presented in a variety of multimedia formats. Furthermore, game-based learning fosters critical thinking and problem-solving skills, keeping children engaged and motivated to learn in this dynamic digital landscape.
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Trisna Mulyati, Prima Denny Sentia, Anis Maulana, and Friesca Erwan. "FATIGUE ANALYSIS OF HIGH DUMP TRUCK OPERATORS IN INDONESIA’S COAL MINING INDUSTRY: A CASE STUDY." Malaysian Journal of Public Health Medicine 20, Special1 (August 1, 2020): 38–44. http://dx.doi.org/10.37268/mjphm/vol.20/no.special1/art.666.
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A coal mining industry typically applies a 24-hours working time, which enforces some workers to stay conscious during night shift, opposing human body's biological clock. This study aims to analyse the level of fatigue experienced by high dump truck operators (HD operators) in a coal mining site in East Kalimantan, Indonesia. This study utilizes primary data which obtained from distributing Industrial Fatigue Research Committee (IFRC) survey to all HD operators and secondary data (for Fatigue Likelihood Scoring - FLS) which consists of HD operators’ working schedule that currently applied in the company. Results obtained is analyzed using Fatigue Risk Management System (FRMS) framework which combines FLS classification and Dawson-McCulloch’s model of fatigue risk trajectory. This study reveals that based on IFRC survey, HD operators experienced low/mild fatigue due to insignificant influence of fatigue-related factors contained in the survey. However, consideration for improvement is in need since the result of fatigue for night shift operators is close to moderate level. In addition, based on FLS, the level of fatigue indicates that HD operators experienced excessive working hours, in which in FRMS graph classified as fatigue-related errors. Thus, this study proposes several strategies as the hazard control mechanism: (1) providing optimum resting time, (2) equipping operators with audio music that lead to positive energy and increasing work focus, and (3) adding afternoon shift to balance the working hours.
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D, Damodharan, Amit Kumar Goel, Krishna Kant Agrawal, Sheradha Johri, and Anuj Kumar. "CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 98–112. http://dx.doi.org/10.17762/ijritcc.v11i4.6392.
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Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection.