Journal articles on the topic 'Diagnosis – Data processing – Congresses'
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Matsumoto, Koushi, Haruaki Sato, Syunji Ohtsuka, Nobutaka Yamada, and Goro Asano. "Computer-assisted data processing of pathogical diagnosis." Journal of Nippon Medical School 59, no. 1 (1992): 75–80. http://dx.doi.org/10.1272/jnms1923.59.75.
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Šverko, Zoran, Ivan Markovinović, Miroslav Vrankić, and Saša Vlahinić. "EEG data processing in ADHD diagnosis and neurofeedback." Engineering review 40, no. 3 (May 21, 2020): 116–23. http://dx.doi.org/10.30765/er.40.3.12.
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In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.
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Sakhno. "Integral data processing systems for functional diagnosis service." Biomedical Engineering 30, no. 1 (1996): 38. http://dx.doi.org/10.1007/bf02383400.
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Sakhno, Yu F., and P. S. Kudryavtsev. "Integral data processing systems for functional diagnosis service." Biomedical Engineering 30, no. 1 (January 1996): 38–42. http://dx.doi.org/10.1007/bf02369227.
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Pasichnyk, Natalya, Renat Rizhniak, and Hanna Deforzh. "Congresses of natural scientists and mathematicians in the “Bulletin of experimental physics and elementary mathematics” (1886–1917): Analysis of publications." History of science and technology 13, no. 2 (December 23, 2023): 280–310. http://dx.doi.org/10.32703/2415-7422-2023-13-2-280-310.
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The article presents the results of a study of publications in the “Bulletin of Experimental Physics and Elementary Mathematics”, about the organization, conduct and results of domestic and foreign congresses of mathematicians and natural scientists, published in Kyiv and Odesa during 1886–1917. The magazine was an unofficial periodical printed publication of the mathematical department of the Novorossiysk Society of Naturalists. The research was conducted with the aim of carrying out a meaningful and quantitative analysis of the texts of journal publications, which highlights the materials of such meetings of scientists and teachers. The authors used scientific methods for meaningful analysis of the research subject, and in the process of quantitative analysis – text quantification, collection of empirical data, their generalization and mathematical and statistical processing. As a result of the research of the magazine’s materials on congresses of mathematicians and natural researchers during the entire period of its publication, the authors came to the following conclusions. The magazine’s materials on conventions and congresses for all the years of its publication accounted for slightly more than 4% of its total area. All National Congresses of Natural Scientists and Mathematicians, which met during the period of publication of the journal, were covered on its pages (this is almost 2/3 of the entire volume of information in the journal about such meetings of scientists and teachers). At the same time, the methods of presenting information and its volume differed significantly in different meetings, depending on the presence of motives of both members of the editorial board. The main motives for placing information were: a) the presence of a pedagogical component in the work of meetings; b) scientific (or pedagogical) significance of reports and meeting participants; c) availability of quality materials about meetings; d) availability of magazine space. International congresses were irregularly covered by the newspaper, the motives for placing information about such meetings in the magazine were similar. Starting in 1901, the editors of the Bulletin introduced the scheme developed during the previous years of the magazine’s existence into the practice of presenting materials about scientific and pedagogical congresses: a) announcement of the event; b) publication of the regulation (statute, program) of the event; c) description of preparation for the event; d) overview of the features of the event; e) presentation of the texts of important speeches. Such a scheme of presentation of meetings was introduced for the first time in popular science and educational periodicals of the Russian Empire. The pedagogical component was the most important motive for both staffs of the Bulletin editorial board when deciding on the features and scope of coverage of materials on the work of domestic and foreign congresses. The materials of Bulletin (and other similar publications of that time) covering the work of domestic and foreign congresses of teachers and researchers of nature and mathematics, which reveal the content of the educational activities of famous scientists and teachers, are an important element of the source base of biographical studies, which conducted by historians of science.
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Ji, Jinjie, Qing Chen, Lei Jin, Xiaotong Zhou, and Wei Ding. "Fault Diagnosis System of Power Grid Based on Multi-Data Sources." Applied Sciences 11, no. 16 (August 20, 2021): 7649. http://dx.doi.org/10.3390/app11167649.
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In order to complete the function of power grid fault diagnosis accurately, rapidly and comprehensively, the power grid fault diagnosis system based on multi-data sources is proposed. The integrated system uses accident-level information, warning-level information and fault recording documents and outputs a complete diagnosis and tracking report. According to the timeliness of three types of information transmission, the system is divided into three subsystems: real-time processing system, quasi-real-time processing system and batch processing system. The complete work is realized through the cooperation between them. While a real-time processing system completes fault diagnosis of elements, it also screens out incorrectly operating protections and circuit breakers and judges the loss of accident-level information. Quasi-real-time system outputs reasons for incorrect actions of protections and circuit breakers under the premise of considering partial warning-level information missing. The batch processing system corrects diagnosis results of the real-time processing system and outputs fault details, including fault phases, types, times and locations of faulty elements. The simulation results and test show that the system can meet actual engineering requirements in terms of execution efficiency and fault diagnosis and tracking effect. It can be used as a reference for self-healing and maintenance of power grids and has a preferable application value.
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S., R., Priyanka S., Jyoti B., and Priyanka K. "Skin Disease Diagnosis System using Image Processing and Data Mining." International Journal of Computer Applications 179, no. 16 (January 17, 2018): 38–40. http://dx.doi.org/10.5120/ijca2018916253.
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Motakabber, S. M. A., Mohammad Mominul Hoque, Md. Rafiqul Islam, Sany Ihsan, Gazi Zahirul Islam, and AHM Zahirul Alam. "MATLAB-Based Vibration Signal Processing for Fault Diagnosis." Asian Journal of Electrical and Electronic Engineering 3, no. 2 (September 30, 2023): 27–32. http://dx.doi.org/10.69955/ajoeee.2023.v3i2.52.
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Traditionally, vibration signal processing has been performed using analog and digital signal analyzers or writing code in intermediate and high-level computer languages. However, the advent of higher-level interpretive-based signal processing software products such as MATLAB has added a new dimension to vibration signal processing. This paper presents a method for analyzing motor vibration data using MATLAB. The method first pre-processes the vibration data to remove noise and baseline wander. Then, the frequency spectrum of the vibration signal is calculated using the Fourier transform. The frequency spectrum is then used to identify the dominant frequencies in the vibration signal. These dominant frequencies can be used to identify potential problems with the motor, such as bearing defects or misalignment. The method was studied on a set of vibration data collected from open source online data of a real motor. The results showed that the method was able to identify the dominant frequencies in the vibration signal accurately. The method was also able to identify the potential problems with the motor. This paper demonstrates the effectiveness of using MATLAB for analyzing motor vibration data. The method presented in this paper can be used to improve the reliability and efficiency of motor maintenance.
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Bakhshi, Ali, Kobra Hajizadeh, Mohammad Reza Tanhayi, and Reza Jamshidi. "Diabetic retinopathy diagnosis using image processing methods." Advances in Obesity, Weight Management & Control 11, no. 5 (September 8, 2022): 132–34. http://dx.doi.org/10.15406/aowmc.2022.12.00375.
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Diabetes mellitus is common disease nowadays which could cause blindness. Earlier detection of diabetes signs from retina fundus images could help predicting and preventing the damages. Image processing methods could process the matrix data of pictures as blood vessel segmentation and exudate detection. In this research, the CLAHE algorithm with morphological transformations are used to blood vessel segmentation and determination of the Hessian matrix of images are utilized to detect the exudate blobs
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Xiao, Yang. "Application of Big Data in Electrical Engineering." Journal of Computing and Electronic Information Management 12, no. 3 (April 30, 2024): 22–27. http://dx.doi.org/10.54097/1cjvmpno.
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Chinese With the continuous progress of science and technology and the rapid development of information technology, big data has become a hot topic in today's society. The application of big data has penetrated into various fields, among which electronic engineering is one of the important application fields. This thesis focuses on the application of big data in electronic engineering and analyses its specific applications in data acquisition and processing, model building and optimization, fault diagnosis and prediction, and intelligent decision-making and control. The study elaborates on the application of big data in electronic engineering. In terms of data acquisition and processing, the applications of sensor data acquisition and processing and signal processing and analysis are highlighted. In the area of model building and optimisation, the paper explores methods for model building and optimisation of electronic devices based on big data. In the area of fault diagnosis and prediction, the paper proposes a fault diagnosis and prediction method based on big data. Finally, in the area of intelligent decision-making and control, the paper discusses intelligent decision-making and control methods based on big data.
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Saeed, Nazia, and Iulia M. Graf. "From Radio Frequency Data to Vascular Diagnosis." Ultrasound 17, no. 3 (August 1, 2009): 131–36. http://dx.doi.org/10.1179/174313409x448598.
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Non-invasive vascular ultrasound has been introduced in patient specific cardio/cerebro-vascular risk evaluation, treatment efficiency estimation, and drug-development strategies. The ultrasound measurement system is based on analysis of radio frequency echo data and is usually combined with evaluations of textural, contextual, morphological and clinical features in a multi-parameter approach. Processing of radio frequency data enables individual vascular risk evaluation depending on the clinical purpose. The non-invasive nature of radio frequency based techniques makes them suitable for screening large populations, and evaluation of drug efficiency in vascular risk reduction.
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Gao, Ming Jun, Guo Yi Zhang, and Hai Cheng Yang. "Research on Multi Agent Data Acquisition and Processing Technology of Distributed Network." Applied Mechanics and Materials 651-653 (September 2014): 2013–19. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2013.
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In this paper, it proposed the function structure of distributed network data acquisition and processing system; based on the analysis of Agent, Agent principle and functional module, according to the characteristic of the servo control system fault diagnosis which is the electro-hydraulic position of the electrical and mechanical equipment, it puts a data acquisition and diagnosis system of Agent network structure, the specific function of the dynamic monitoring control Agent and fault identification Agent, it establishes their models and discusses the multi Agent coordination mechanism, the task decomposition and the control strategy of fault diagnosis.
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Czajkowska, Joanna, and Martyna Borak. "Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review." Sensors 22, no. 21 (October 30, 2022): 8326. http://dx.doi.org/10.3390/s22218326.
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Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.
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Dr. Selvarani Rangasamy, Mrs Disha Sushant Wankhede,. "REVIEW ON DEEP LEARNING APPROACH FOR BRAIN TUMOR GLIOMA ANALYSIS." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 1, 2021): 395–408. http://dx.doi.org/10.17762/itii.v9i1.144.
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Brain tumor diagnosis has evolved as a very critical need in current medical diagnosis. Early diagnosis of tumor detection is an important need for the primitive treatment of brain tumor patient increasing the survival rate of patient. MRI diagnosis of brain tumor for cancer treatment is a large processing due to volumetric content of scan sample. The processing of clinical data is large and consumes a high processing time. Hence, the need of early diagnosis and proper segmentation of brain tumor region is in need. This paper outlines a review on the developments of MRI sample processing for early diagnosis for brain tumor glioma diagnosis using deep learning approach. The advantage of learning capability and finer processing efficiency has gained an advantage in MRI image processing, which enable a better processing efficiency and accuracy in early diagnosis. Deep learning approach has shown a benefit of image coding based on selective features and state of art processing in diagnosis. The evaluation objective of the MRI sample processing has shown a better accuracy than the comparative existing approaches. The recent trends, the advantages and limitation of the existing approach for MRI diagnosis is outlined.
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Yang, Xiaoqiang. "Remote Diagnosis and Detection Technology for Electrical Control of Intelligent Manufacturing CNC Machine Tools." Scientific Programming 2022 (October 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/4642550.
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An intelligent manufacturing environment employs internet-based communication and monitoring technologies for fault detection, diagnosis, and monitoring of industrial machines. The monitoring and fault detection are performed remotely without human intervention that predicts faults and ensures specific operational control. This article introduces a rational fault diagnosis process (RFDP) best suited for remote fault detection and diagnosis of CNC machine tools. The proposed process monitors different operational segments of the machine and extracts related data to validate its performance. The interconnection between the segments and fault impact are identified using the transfer learning process. The previously identified faults are used in the state training process to improve detection and diagnosis accuracy. Depending on the operational control continuity, the performance is assessed post the fault diagnosis. The learning paradigm is trained using the machine’s efficiency and rational data processing to predict the transfer states’ faults. The transfer states are modulated based on the efficiency and minimum-maximum control recommended for the CNC machine. This process’s performance is validated using detection accuracy, diagnosis recommendation, downtime, data processing rate, and processing time. From the experimental analysis, it is seen that for the varying data extraction rates, the proposed process improves detection accuracy by 10.14%, diagnosis recommendation by 8.58% and data processing rate by 7.95%, reducing the downtime by 8.85%, and processing by 11.24%.
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Yousif, Thanaa Hasan, Nahla Ali Tomah, and Marwa Jaleel Mohsin. "Machine learning-based diagnosis of eye-diseases." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 2 (November 1, 2023): 787. http://dx.doi.org/10.11591/ijeecs.v32.i2.pp787-795.
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<span>Over the last several years, artificial intelligence (AI) has been substantially utilized in image processing and classification. Several tools are accessible for visualizing, training, and pre-processing image data. One such tool is orange, which has several pre-processing modules and a particular add-on for image processing methods in addition to excellent data visualization. The tool (version 3.32.0) was used in the suggested study to give a comparative and predictive analysis using several classification models. Three main models have been used to train and predict the three groups image eye diseases. The results were compared based on some criteria, including area-under-a-curve (AUC), the accuracy of classification (CA), F1 score, precision, and recall. These models include K-nearest neighbour (KNN), logistic regression (LR), artificial neural networks (ANN) and stacking model. The stacking model, which is a novel model, is also presented in this work by concatenating the output of the parallel form of ANN and KNN models with the LR model. The best performance belonged to the Stacking model, which offers the best detection and prediction results.</span>
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Hou, Liqun, Junteng Hao, Yongguang Ma, and Neil Bergmann. "IWSNs with On-Sensor Data Processing for Energy Efficient Machine Fault Diagnosis." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 08 (May 14, 2019): 42. http://dx.doi.org/10.3991/ijoe.v15i08.10314.
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<span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">Machine fault diagnosis systems need to collect and transmit dynamic signals, like vibration and current, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. Large amounts of transmission data will </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">increase the energy consumption and </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">shorten the lifetime of energy-constrained IWSN node</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s as well</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">.</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">To address th</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">e</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> tension</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">s</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US"> when implementing machine fault diagnosis applications in </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">IWSNs</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">, this paper proposes a</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">n</span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 宋体; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast;" lang="EN-US">energy efficient </span><span style="color: black; font-family: 'Times New Roman','serif'; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.</span>
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Elorza, Iker, Iker Arrizabalaga, Aritz Zubizarreta, Héctor Martín-Aguilar, Aron Pujana-Arrese, and Carlos Calleja. "A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis." Energies 15, no. 1 (December 21, 2021): 33. http://dx.doi.org/10.3390/en15010033.
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Modern wind turbines depend on their blade pitch systems for start-ups, shutdowns, and power control. Pitch system failures have, therefore, a considerable impact on their operation and integrity. Hydraulic pitch systems are very common, due to their flexibility, maintainability, and cost; hence, the relevance of diagnostic algorithms specifically targeted at them. We propose one such algorithm based on sensor data available to the vast majority of turbine controllers, which we process to fit a model of the hydraulic pitch system to obtain significant indicators of the presence of the critical failure modes. This algorithm differs from state-of-the-art, model-based algorithms in that it does not numerically time-integrate the model equations in parallel with the physical turbine, which is demanding in terms of in situ computation (or, alternatively, data transmission) and is highly susceptible to drift. Our algorithm requires only a modest amount of local sensor data processing, which can be asynchronous and intermittent, to produce negligible quantities of data to be transmitted for remote storage and analysis. In order to validate our algorithm, we use synthetic data generated with state-of-the-art aeroelastic and hydraulic simulation software. The results suggest that a diagnosis of the critical wind turbine hydraulic pitch system failure modes based on our algorithm is viable.
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Wang, Yongfu, Gaochang Wu, Gang (Sheng) Chen, and Tianyou Chai. "Data mining based noise diagnosis and fuzzy filter design for image processing." Computers & Electrical Engineering 40, no. 7 (October 2014): 2038–49. http://dx.doi.org/10.1016/j.compeleceng.2014.06.010.
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Liu, Bai Lin, and Lei Li. "Development of Integrated Testing Instrument for Mechatronic System." Advanced Materials Research 605-607 (December 2012): 1436–39. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1436.
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In order to improve the effectiveness of fault diagnosis for mechatronic system, new integrated testing instrument was developed. The testing instrument was integrated with experience, detected data and complex technical principles. The structure of the integrated testing instrument was introduced. The system is divided into fault diagnosis expert system and signal processing device. Fault diagnosis expert system software is to complete the human-computer interaction, testing process control, test result analysis processing, output display and fault diagnosis. Signal processing instrument includes test signal acquisition, signal conditioning, data acquisition, and data communication. Experiments show that the instrument can find the fault efficiently and improve the maintenance efficiency of a certain type of mechatronic system.
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Mota-Gutiérrez, Cecilia Guadalupe, Edgar Omar Reséndiz-Flores, and Yadira Iracema Reyes-Carlos. "Mahalanobis-Taguchi system: state of the art." International Journal of Quality & Reliability Management 35, no. 3 (March 5, 2018): 596–613. http://dx.doi.org/10.1108/ijqrm-10-2016-0174.
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Purpose The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields. Design/methodology/approach The Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making. Findings Its scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses. Originality/value This paper is a summary of the main applications, contributions and changes to MTS.
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Vahabi, Arman, Funda Gül, Sabina Garakhanova, Hilal Sipahi, and Oğuz Reşat Sipahi. "Pooled analysis of 1270 infective endocarditis cases in Turkey." Journal of Infection in Developing Countries 13, no. 02 (February 28, 2019): 93–100. http://dx.doi.org/10.3855/jidc.10056.
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Introduction: Despite developments in medicine, infective endocarditis (IE) is still associated with significant morbidity and mortality. In this study it was aimed to systematically review the infective endocarditis literature published or presented from Turkey.
Methods: To find the published series, one national database (Ulakbim), and three international databases (Scopus, Pubmed and Sci-e) were searched between 31 October-3 November 2014. also, abstracts of congresses by three national congresses were searched for studies regarding infective endocarditis.
Results: Data for 1270 patients (38.3% female, mean age 46.2, 28% prosthetic valve endocarditis) with a diagnosis of infective endocarditis were obtained from 21 reports (18 published articles and three congress abstracts). Of the 18 articles, four were in peer-reviewed medical journals indexed in national databases and 14 were in international databases. There was an underlying heart disease in 51.9% and history of dental procedure was 6.7%. Fever, heart murmur and fatigue were present in 94%, 71.4% and 69% respectively. most commonly involved site was mitral valve (43.3%), followed by aortic (33.8%) and tricuspid valve (6.4%). Staphylococcus aureus, coagulase-negative staphylococci and enterococci comprised the 22.8%, 9.7% and 7.5% of the cases while 31.1% were culture-negative. Overall mortality was 23.4%. When we compared series related to years 2008 and before and 2009 and after, the mortality rates were (24.1%-224/931) vs (20.1%-32/159), respectively (p = 0,31).
Conclusion: Infective endocarditis is still associated with significant mortality. S. aureus seems to be the most common etiologic agent. There was a slight decrease in the recent years in mortality.
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Chirkov, Andrey, Larisa Gagarina, Nikolay Mironov, and Roman Lipovy. "Diagnosis automation methods in the subject area." Automation and modeling in design and management 2022, no. 4 (December 21, 2022): 37–45. http://dx.doi.org/10.30987/2658-6436-2022-4-37-45.
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This article gives an overview of some existing diagnostic automation methods applicable to a variety of subject areas. At present, many branches of industry, medicine, agriculture and agrotechnical economies are moving towards reducing the need for involving human resources in the processes of diagnosing equipment malfunctions, various diseases of both people and plants. The number of different methods for diagnostics and processing the received data increases over time, as do the data flow itself and the requirements for the processing accuracy and speed. An important task is to build adequate models for data analysis, taking into account random perturbations and the need for rapid research at the rate of incoming data. The only way to choose the most optimal method is to conduct a comparative analysis and correlate many factors to be considered when choosing a particular method
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Anjum, Mohd, Hong Min, and Zubair Ahmed. "Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis." Bioengineering 11, no. 6 (May 24, 2024): 539. http://dx.doi.org/10.3390/bioengineering11060539.
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There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.
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Li, Chengtai, Yiming Zhang, Ying Weng, Boding Wang, and Zhenzhu Li. "Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology." Diagnostics 13, no. 2 (January 12, 2023): 286. http://dx.doi.org/10.3390/diagnostics13020286.
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In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper.
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Li, Lian Tian. "The Application of Fuzzy Data Processing Technology in the Network Failure Mining." Applied Mechanics and Materials 380-384 (August 2013): 1073–76. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1073.
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In order to solve the coordinate problem of high redundancy and lack of stability in traditional failure mining knowledge base, this paper presents a fuzzy failure mining algorithm to realize regular acquisition in inconsistent case and purification of learning samples by the comprehensive application of fuzzy data processing technology. With characters of simplified samples, strong adaptability, high failure tolerance and not easy to fall into local minimum point, the algorithm can effectively process the diagnosis and incompatible information in network failure mining. The experiments show that the system implemented by this method improves accuracy and speed of diagnosis and has a certain application value comparing to other similar methods.
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Berisha, Sebastian, Shengyuan Chang, Sam Saki, Davar Daeinejad, Ziqi He, Rupali Mankar, and David Mayerich. "SIproc: an open-source biomedical data processing platform for large hyperspectral images." Analyst 142, no. 8 (2017): 1350–57. http://dx.doi.org/10.1039/c6an02082h.
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Chmielewski, Mariusz, Damian Frąszczak, and Dawid Bugajewski. "Architectural concepts for managing biomedical sensor data utilised for medical diagnosis and patient remote care." MATEC Web of Conferences 210 (2018): 05016. http://dx.doi.org/10.1051/matecconf/201821005016.
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This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.
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Li, Jinwen. "Signal processing and thermal performance analysis of motor heat recovery system." Thermal Science 27, no. 2 Part A (2023): 1125–31. http://dx.doi.org/10.2298/tsci2302125l.
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In order to realize the condition monitoring of the motor ?anytime, anywhere?, improve the detection accuracy and shorten the detection time, the author proposes a fault signal processing and diagnosis system for the motor heat recovery system based on the IoT. That is, based on the IoT technology, a mobile terminal oriented motor remote monitoring and fault diagnosis system, the sensing layer of the system collects real-time motor operation status data, and the transmission layer realizes data transmission, cloud storage and response to data requests from the application layer, finally, at the mobile end, the motor running status and diagnosis results are displayed through charts and text, so as to realize remote monitoring and fault diagnosis of the motor. The experimental results show that the accuracy of fault diagnosis test of GA-SVM in mobile terminal is more than 90%, and the running time is less than 30 ms, and the running time is very short. It proves that the mobile terminal uses the fault detection method based on GA-SVM model with high accuracy and short detection time, that is, the fault signal processing and diagnosis accuracy of the motor heat recovery system of the IoT is high and the detection time is short.
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Dath, Mogalraj Kushal, Nahida Nazir, Amita Dhankhar, Kamna Solanki, and Omdev Dahiya. "Malarial Diagnosis with Deep Learning and Image Processing Approaches." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (May 17, 2023): 210–22. http://dx.doi.org/10.17762/ijritcc.v11i5s.6647.
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Malaria is a mosquito-borne disease that has killed an estimated a half-a-million people worldwide since 2000. It may be time consuming and costly to conduct thorough laboratory testing for malaria, and it also requires the skills of trained laboratory personnel. Additionally, human analysis might make mistakes. Integrating denoising and image segmentation techniques with Generative Adversarial Network (GAN) as a data augmentation technique can enhance the performance of diagnosis. Various deep learning models, such as CNN, ResNet50, and VGG19, for recognising the Plasmodium parasite in thick blood smear images have been used. The experimental results indicate that the VGG19 model performed best by achieving 98.46% compared to other approaches. This study demonstrates the potential of artificial intelligence to improve the speed and precision of pathogen detection which is more effective than manual analysis.
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Wei, Ren Sheng. "Intelligent Construction Machinery Fault Diagnosis Based on Multiple Intelligent Machines." Applied Mechanics and Materials 340 (July 2013): 484–88. http://dx.doi.org/10.4028/www.scientific.net/amm.340.484.
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With the rapid development of science technology, to realize digital monitoring in the complex working conditions, the use of artificial intelligence machine to quickly achieve control operations. However, in the artificial intelligence system process, there may be a programmable datas inaccuracy and non real time, thereby to bring the certain error of control system that affects people's judgment. In programming data acquisition processing system, to introduce CBR diagnosis technique application, existing programming data acquisition processing system carries on fault coupling analysis. Through the data programming module transform for real-time diagnosis module, using artificial intelligence carries on automatic diagnosis for programming data, thus effectively solving the deviation in programming data acquisition process.
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Tran, Quang Thinh, and Sy Dzung Nguyen. "Bearing Fault Diagnosis Based on Measured Data Online Processing, Domain Fusion, and ANFIS." Computation 10, no. 9 (September 8, 2022): 157. http://dx.doi.org/10.3390/computation10090157.
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Processing noise online in sensors-based measurement data (SMD) and mitigating the effect of domain drift are always challenges. As a result, it negatively impacts the effectiveness and feasibility of data-driven model (DDM)-based mechanical-system fault identification (MFI). Here, we propose an online bearing fault diagnosis method named ANFIS-BFDM by using an adaptive neurofuzzy inference system (ANFIS). Reduction in the influence of domain drift between the source domain and target domain (DDSTD) is considered in both the data processing and fault identification. Online solutions for preprocessing SMD and exploiting the filtered data to label the target domain are presented in a fusion domain deriving from the source and target domains. First, in the offline phase, frequency-based splitting of SMD into different time series is performed to cancel the high-frequency region. An optimal data screening threshold (ODST) is distilled in the remaining low-frequency data to develop an impulse noise filter named FIN. An ANFIS then identifies the dynamic response of the bearing(s) via the filtered data. The FIN and ANFIS are finally exploited during the online phase to filter noise and recognize the object’s health status online. The survey results reflect the positive effects of the method, even if severe impulse noise appears in the databases.
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Fang, Haocheng, Kang Zhou, Yilin Zou, Weilin Deng, Xin He, and Jiapeng Zhou. "A diagnosis optimization system for grain processing based on multiple data analysis algorithms." Systems Science & Control Engineering 7, no. 2 (September 17, 2019): 96–107. http://dx.doi.org/10.1080/21642583.2019.1666318.
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Humeau-Heurtier, Anne, Edite Figueiras, and Joao Cardoso. "Signal and Image Processing of Physiological Data: Methods for Diagnosis and Treatment Purposes." Computational and Mathematical Methods in Medicine 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/1054605.
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Zheng, Wei, Xueqing Zhao, Ming Zhang, Feiran Hu, Yinan Zhou, Fei Xie, and Yuan Pan. "NI P2P powered multi FPGA real-time data processing on tokamak diagnosis systems." Fusion Engineering and Design 146 (September 2019): 1496–99. http://dx.doi.org/10.1016/j.fusengdes.2019.02.114.
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Morlier, Joseph, Frédéric Bos, and Patrick Castéra. "Diagnosis of a portal frame using advanced signal processing of laser vibrometer data." Journal of Sound and Vibration 297, no. 1-2 (October 2006): 420–31. http://dx.doi.org/10.1016/j.jsv.2006.03.044.
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Cao, Yuan, Peng Li, and Yuzhuo Zhang. "Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing." Future Generation Computer Systems 88 (November 2018): 279–83. http://dx.doi.org/10.1016/j.future.2018.05.038.
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Habbouche, Houssem, Tarak Benkedjouh, Yassine Amirat, and Mohamed Benbouzid. "Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach." Entropy 23, no. 6 (May 31, 2021): 697. http://dx.doi.org/10.3390/e23060697.
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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.
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Tene Koyazo, Jacques, Moise Avoci Ugwiri, Aimé Lay-Ekuakille, Maria Fazio, Massimo Villari, and Consolatina Liguori. "Collaborative systems for telemedicine diagnosis accuracy." ACTA IMEKO 10, no. 3 (September 30, 2021): 192. http://dx.doi.org/10.21014/acta_imeko.v10i3.1133.
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The transmission of medical data and the possibility for distant healthcare structures to share experiments about a given medical case raises several conceptual and technical questions. Good remote healthcare monitoring deals with more problems in personalized heath data processing compared to the traditional methods nowadays used in several parts of hospitals in the world. The adoption of telemedicine in the healthcare sector has significantly changed medical collaboration. However, to provide good telemedicine services through new technologies such as cloud computing, cloud storage, and so on, a suitable and adaptable framework should be designed. Moreover, in the chain of medical information exchange, between requesting agencies, including physicians, a secure and collaborative platform enhanced the decision-making process. This paper provides an in-depth literature review on the interaction that telemedicine has with cloud-based computing. On the other hand, the paper proposes a framework that can allow various research organizations, healthcare sectors, and government agencies to log data, develop collaborative analysis, and support decision-making. The electrocardiogram (ECG) and electroencephalogram EEG case studies demonstrate the benefit of the proposed approach in data reduction and high-fidelity signal processing to a local level; this can make possible the extracted characteristic features to be communicated to the cloud database.
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Enciso-Salas, Luis, Gustavo Pérez-Zuñiga, and Javier Sotomayor-Moriano. "Fault Diagnosis via Neural Ordinary Differential Equations." Applied Sciences 11, no. 9 (April 22, 2021): 3776. http://dx.doi.org/10.3390/app11093776.
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Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data.
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Zhang, Li, Zhi Jian Liu, Da Lin Sun, and Dian Hua Yang. "Fault Diagnosis Based on Qualitative Data by Gray System Theory." Applied Mechanics and Materials 644-650 (September 2014): 1227–29. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1227.
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Gray system theory is good at dealing with the uncertain problems with a little amount of data. Some equipment’s fault data is abundant, and fault phenomenon is not explicit, uncertain and qualitative. So, it is difficult to diagnosing this kind fault through the traditional methods in a short time. This paper analyzes the data’s feature and then makes quantitative processing. The result shows that by this model, fault diagnosis can be simplified and quickly. And it is proved that the new kind of fault-positioning method for quick diagnosis is effective and efficient.
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Aaraji, Zahraa S., and Hawraa H. Abbas. "Automatic Diagnosis of Alzheimer’s Disease Using Deep Learning Techniques." NeuroQuantology 19, no. 11 (December 11, 2021): 126–40. http://dx.doi.org/10.14704/nq.2021.19.11.nq21183.
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Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).
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Jia, Jingbo, Peng Wu, and Hussain Dawood. "An Improved CTGAN for Data Processing Method of Imbalanced Disk Failure." International Journal on Cybernetics & Informatics 12, no. 6 (October 7, 2023): 69–81. http://dx.doi.org/10.5121/ijci.2023.120606.
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To address the problem of insufficient failure data generated by disks and the imbalance between the number of normal and failure data. The existing Conditional Tabular Generative Adversarial Networks(CTGAN) deep learning methods have been proven to be effective in solving imbalance disk failure data. But CTGAN cannot learn the internal information of disk failure data very well. In this paper, a fault diagnosis method based on improved CTGAN, a classifier for specific category discrimination is added and a discriminator generate adversarial network based on residual network is proposed. We named it Residual Conditional Tabular Generative Adversarial Networks (RCTGAN). Firstly, to enhance the stability of system a residual network is utilized. RCTGAN uses a small amount of real failure data to synthesize fake fault data; Then, the synthesized data is mixed with the real data to balance the amount of normal and failure data; Finally, four classifier (multilayer perceptron, support vector machine, decision tree, random forest) models are trained using the balanced data set, and the performance of the models is evaluated using G-mean. The experimental results show that the data synthesized by the RCTGAN can further improve the fault diagnosis accuracy of the classifier.
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Mahoto, Naeem Ahmed, Asadullah Shaikh, Adel Sulaiman, Mana Saleh Al Reshan, Adel Rajab, and Khairan Rajab. "A machine learning based data modeling for medical diagnosis." Biomedical Signal Processing and Control 81 (March 2023): 104481. http://dx.doi.org/10.1016/j.bspc.2022.104481.
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P, Rajesh, Murugan A, Murugamantham B, and Ganesh Kumar S. "Lung Cancer Diagnosis and Treatment Using AI and Mobile Applications." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 17 (October 13, 2020): 189. http://dx.doi.org/10.3991/ijim.v14i17.16607.
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Cancer has become very common in this evolving world. Technology advancements, increased radiations have made cancer a common syndrome. Various types of cancers like Skin Cancer, Breast Cancer, Prostate Cancer, Blood Cancer, Colorectal cancer, Kidney Cancer and Lung Cancer exits. Among these various types of cancers, the mortality rate is high in lung cancer which is tough to diagnose and can be diagnosed only in advanced stages. Small cell lung cancer and non-small cell lung cancer are the two types in which non-small cell lung cancer (NSCLC) is the most common type which makes up to 80 to 85 percent of all cases [1]. Digital Image Processing and Artificial Intelligence advancements has helped a lot in medical image analysis and Computer Aided Diagnosis(CAD). Numerous research is carried out in this field to improve the detection and prediction of the cancerous tissues. In current methods, traditional image processing techniques is applied for image processing, noise removal and feature extraction. There are few good approaches that applies Artificial Intelligence and produce better results. However, no research has achieved 100% accuracy in nodule detection, early detection of cancerous nodules nor faster processing methods. Application of Artificial Intelligence techniques like Machine Learning, Deep Learning is very minimal and limited. In this paper [Figure 1], we have applied Artificial intelligence techniques to process CT (Computed Tomography) Scan image for data collection and data model training. The DICOM image data is saved as numpy file with all medical information extracted from the files for training. With the trained data we apply deep learning for noise removal and feature extraction. We can process huge volume of medical images for data collection, image processing, detection and prediction of nodules. The patient is made well aware of the disease and enabled with their health tracking using various mobile applications made available in the online stores for iOS and Android mobile devices.
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Ozcift, Akin, and Arif Gulten. "Assessing Effects of Pre-Processing Mass Spectrometry Data on Classification Performance." European Journal of Mass Spectrometry 14, no. 5 (April 1, 2008): 267–73. http://dx.doi.org/10.1255/ejms.938.
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Disease prediction through mass spectrometry (MS) data is gaining importance in medical diagnosis. Particularly in cancerous diseases, early prediction is one of the most life saving stages. High dimension and the noisy nature of MS data requires a two-phase study for successful disease prediction; first, MS data must be pre-processed with stages such as baseline correction, normalizing, de-noising and peak detection. Second, a dimension reduction based classifier design is the main objective. Having the data pre-processed, the prediction accuracy of the classifier algorithm becomes the most significant factor in the medical diagnosis phase. As health is the main concern, the accuracy of the classifier is clearly very important. In this study, the effects of the pre-processing stages of MS data on classifier performances are addressed. Three pre-processing stages—baseline correction, normalization and de-noising—are applied to three MS data samples, namely, high-resolution ovarian cancer, low-resolution prostate cancer and a low-resolution ovarian cancer. To measure the effects of the pre-processing stages quantitatively, four diverse classifiers, genetic algorithm wrapped K-nearest neighbor (GA-KNN), principal component analysis-based least discriminant analysis (PCA-LDA), a neural network (NN) and a support vector machine (SVM) are applied to the data sets. Calculated classifier performances have demonstrated the effects of pre-processing stages quantitatively and the importance of pre-processing stages on the prediction accuracy of classifiers. Results of computations have been shown clearly.
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Kandpal, Jyoti. "Exploring the Potential of Wearable Electronics for Healthcare Monitoring and Diagnosis." Mathematical Statistician and Engineering Applications 71, no. 2 (March 6, 2022): 658–69. http://dx.doi.org/10.17762/msea.v71i2.2195.
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Chronic diseases kill many Humans in all over the world. Monitor risk factors including physical exercise to manage these illnesses. Wearables like Fitbit can track and give health data to help users make decisions. Most wearables marketing targets the young, active, and most populous racial groups. Wearable electronics can revolutionize healthcare by continuously monitoring health factors. Sensor technology, data processing, and communication protocols have made wearable gadgets useful for healthcare monitoring and diagnosis. This article discusses sensors, data processing, and communication protocols used in wearable electronics to revolutionize healthcare monitoring and diagnosis. A side-by-side table compares each method's pros and cons. The topic covers wearable electronics processing for healthcare monitoring and diagnosis. A block architecture and graphic explain healthcare monitoring and diagnosis using wearable electronics. Wearable electronics adoption is often hampered by concerns regarding data privacy and security, data reliability, and healthcare system compatibility. Wearable electronics are revolutionizing medicine in numerous ways, from monitoring chronic illnesses to giving emergency treatment. Wearable tech could develop into artificial intelligence, machine learning, augmented reality, virtual reality, cutting-edge sensors, telemedicine, 5G networks, nanotechnology, and blockchain. Finally, wearable electronics research could improve patient outcomes and quality of life, transforming healthcare.
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Jarlier, Frédéric, Nicolas Joly, Nicolas Fedy, Thomas Magalhaes, Leonor Sirotti, Paul Paganiban, Firmin Martin, Michael McManus, and Philippe Hupé. "QUARTIC: QUick pArallel algoRithms for high-Throughput sequencIng data proCessing." F1000Research 9 (April 6, 2020): 240. http://dx.doi.org/10.12688/f1000research.22954.1.
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Life science has entered the so-called ’big data era’ where biologists, clinicians and bioinformaticians are overwhelmed with unprecedented amount of data. High-throughput sequencing has revolutionized genomics and offers new insights to decipher the genome structure. However, using these data for daily clinical practice care and diagnosis purposes is challenging as the data are bigger and bigger. Therefore, we implemented software using Message Passing Interface such that the alignment and sorting of sequencing reads can easily scale on high-performance computing architecture. Our implementation makes it possible to reduce the time to delivery to few minutes, even on large whole-genome data using several hundreds of cores.
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Zhang, Deng Pan, Hong Li Zhu, and Yong Gang Shi. "Data Communication Methods for the Fault Diagnosis Instrument System." Applied Mechanics and Materials 42 (November 2010): 386–90. http://dx.doi.org/10.4028/www.scientific.net/amm.42.386.
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The data communication plays a very important role in fault diagnosis instrument system. In this paper, a flexible software bus method is proposed which is designed for the communication among instrument components. In this way, the data processing modules can be prefabricated together dynamically and in parallel. To carry out the communication between the bus and components, the design principle and the architecture of the components is discussed, and then the fault diagnosis instrument cases can be constructed by configuring the instrument components with the communication addresses. Based on the software bus, the processed data of fault diagnosis can propagate among the configured data flow ways asynchronously, and users can plug and unplug any components to the instrument platform. The application cases shows that the proposed way can accelerate data exchange among the modules based on the components and improve the working processes efficiency.
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Li, Yang, Yan Qiang Li, and Zhi Xue Wang. "Fault Diagnosis of Automobile ECUs with Data Mining Technologies." Applied Mechanics and Materials 40-41 (November 2010): 156–61. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.156.
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With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.