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9th International Conference on Automatic Processing of Natural-Language Electronic Texts with NooJ (NooJ)

作者： Reentovich, Ivan Hetsevich, Yuras Voronovich, Valery Kachan, Evgenia Kozlovskaya, Hanna Tretyak, Angelina Koshchanka, Uladzimir United Inst Informat Problems Minsk BELARUS Belarusian State Univ Minsk BELARUS Natl Acad Sci Belarus Ctr Belarusian Culture Language & Literature Res Minsk BELARUS

ISBN: (纸本)9783319424712;9783319424705

In this article the first one-million corpus for the Belarusian NooJ module is represented. The given corpus has been built up of texts, patched up into sections by different subject categories. From the broad list of possible subject categories in the sections the corpus focuses on fiction, historic, medical, scientific, sociological literature, etc. Given a great number of similar subject categories, the first one-million corpus can be considered as a first subject collection of texts for the Belarusian NooJ module. The text corpus is expected to be suitable for research in the following aspects: word polysemy processing of various texts, polysemic punctuation marks processing, and a new lexical items search. The first one-million corpus for the Belarusian NooJ module can be fully applicable in many fields of linguistic research.

关键词： Corpora Belarusian NooJ-module Statistical analysis Part-of-Speech tagging machine-learning algorithms Levenshtein algorithm The machine-learning model Counter-check Spelling errors Concatenation-in-paradigm Unknown words search Known words search Clustering Belarusian N-Corpus Text processing

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Data exchanges based on blockchain in m-Health applications 10

Data exchanges based on blockchain in m-Health applications

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10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 9th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH)

作者： Clim, Antonio Zota, Razvan Daniel Constantinescu, Radu Bucharest Univ Econ Studies 15-17 Dorobanti Bucharest 010552 Romania

The most important aspect of handling data in the healthcare industry is its seamless and secure transition across intercepting nodes. Effective elimination of third-party entities and ensuring direct links between patient and healthcare provider can result in the transmission of error-free, unduplicated data. The use of blockchains can open up opportunities to counter the current requirements due to their ability to safely share information across nodes and networks from the access point and secure the safety of transactions. Currently, sharing medical data is observed to be slow, incomplete, insecure, and provider-centric. These shortcomings prevent data interoperability and are a consequence of lack of foundational, structural, and semantic inoperability. By applying the blockchain technologies with appropriate markers, the safety of patient data can be ensured during data transmission. This paper evaluates the potential use of blockchain technology in association with mobile-based healthcare applications. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： mobile healthcare blockchain technology data exchange machine-learning algorithms

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EARLY DETECTION OF COVID-19 DISEASE USING COMPUTED TOMOGRAPHY IMAGES AND OPTIMIZED CNN-LSTM 17

EARLY DETECTION OF COVID-19 DISEASE USING COMPUTED TOMOGRAPH...

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17th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Memon, Muhammad Hammad Golilarz, Noorbakhsh Amiri Li, Jianping Yazdi, Mohammad Addeh, Abdoljalil Yibin Univ Fac Artificial Intelligence & Big Data Yibin 644000 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

ISBN: (纸本)9780738142593

Since the novel Coronavirus (COVID-19) pandemic showed up in China, it became a big problem for health authorities to counter this life-threatening disease. Early light signs such as fever and nonproductive cough give a chance for early detection of disease and appropriate treatment. Imaging features that can be obtained using computed tomography (CT) images are of the most significant aspects of COVID-19 for screening, examination, therapy evaluation, and follow-up. This paper proposes an intelligent method for early detection of COVID-19 based on CT images and deep neural networks. In the developed method, the convolutional neural network (CNN) is used for automatic feature extraction from CT images and long-short term memory (LSTM) is used for final classification. Moreover, the Harris hawk optimization (HHO) algorithm is implemented for finding the best possible value of internal parameters of CNN and LSTM, such as the number of convolution/pooling layers, size, and the number of convolution kernels with the aim of increasing the classification accuracy. The developed method tested on data collected in Mashi Daneshvari Hospital in Iran. The obtained results showed that the developed method could detect the COVID-19 with high accuracy without needing radiologist experts.

关键词： CNN LSTM Biomedical image processing machine-learning algorithms Optimization

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The effect of hazard shock and disclosure information on property and land prices: a machine-learning assessment in the case of Japan

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REVIEW OF REGIONAL RESEARCH-JAHRBUCH FUR REGIONALWISSENSCHAFT 2021年第1期41卷 1-32页

作者： Peng, Ti-Ching Natl Taipei Univ Dept Real Estate & Built Environm 151Univ Rd New Taipei 23741 Taiwan

The occurrence of 6.7-magnitude Hokkaido Eastern Iburi Earthquake on 6th September 2018 made local residents in Japan realise the underneath hazardous threat to their own safety and residential dwellings. Japan has faced various natural disasters for a long time, and Government chose to unveil these hazard data in details to the public to raise every one's alertness. Despite their long-term awareness of hazards, this short-term unexpected event should have impact on local real estate markets. Given the transaction data of properties and lands, this study used machine-learning algorithms to examine whether the unexpected hazard shock (i.e. 2018 Hokkaido Earthquake) or the observed local hazard information in geographic areas (i.e. long-term evaluation of each real estate) would be capitalised into the prices of residential properties and lands in the case of Hokkaido, Japan. It is assumed that the difference in the characteristics of disaster notification would alter individuals' risk perception and thus the evaluation of properties;compared to uncertain, infrequent occurrence of earthquakes in the unknown future (i.e. beyond the scope of objective estimation), the release of long-term hazardous information (tied to each property or land), which is more perceivable in near future, is more likely to be capitalised into real estate. It is found that housing/land attributes are still the key features to real estate values in Hokkaido. In comparison with short-term unexpected hazard shock (i.e. 2018 Earthquake), the long-term hazard threats are more influential to prices of properties and lands. This is possibly due to people's awareness of the hazard conditions of local communities while deciding where to reside.

关键词： Natural hazard Japan Property prices Land prices machine-learning algorithms

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Evicase: An Evidence-based Case Structuring Approach for Personalized Healthcare

Evicase: An Evidence-based Case Structuring Approach for Per...

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24th Medical Informatics in Europe Conference (MIE)

作者： Carmeli, Boaz Casali, Paolo Goldbraich, Anna Goldsteen, Abigail Kent, Carmel Licitra, Lisa Locatelli, Paolo Restifo, Nicola Rinott, Ruty Sini, Elena Torresani, Michele Waks, Zeev IBM Res Haifa Haifa Israel Ist Nazl Tumori Milan Italy Fdn Poltecn Milano Milan Italy

ISBN: (纸本)9781614991014

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

关键词： Decision Support Personalized Medicine Clinical Guidelines Clinical Business Intelligence machine-learning algorithms

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Use of ensemble methods for indirect test of RF circuits: can it bring benefits? 20

Use of ensemble methods for indirect test of RF circuits: ca...

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20th IEEE Latin American Test Symposium (LATS)

作者： El Badawi, H. Azais, F. Bernard, S. Comte, M. Kerzerho, V. Lefevre, F. Univ Montpellier CNRS LIRMM 161 Rue Ada F-34095 Montpellier France NXP Semicond 2 Esplanade Anton Philips F-14000 Caen France

ISBN: (纸本)9781728117560

Indirect testing of analog and RF integrated circuits is a widely studied approach, which has the benefits of relaxing requirements on test equipment and reducing industrial test cost. It is based on machine-learning algorithms to train a regression model that maps an indirect and low-cost measurement space to the performance parameter space. In this work, we explore the benefit of using ensemble learning. Rather than using one single model to estimate targeted parameters, ensemble learning consists of training multiple individual regression models and combining their outputs in order to improve the predictive power. Different ensemble methods based on bagging, boosting or stacking are investigated and compared to classical individual models. Results are illustrated and discussed on three RF performances of a LNA for which we have production test data.

关键词： indirect testing RF integrated circuits machine-learning algorithms ensemble methods test efficiency

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Epileptic seizures prediction of pediatric subjects in health-care pervasive environments

Epileptic seizures prediction of pediatric subjects in healt...

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IEEE International Conference on Communications (IEEE ICC)

作者： Skoric, Tamara Bajic, Dragana Univ Novi Sad Fac Tech Scientist Novi Sad Serbia

ISBN: (纸本)9798350333077

The pervasive healthcare system aims to provide health services to the end-user. Recording the electroencephalograph (EEG) data out of clinical/laboratory conditions and its wireless transmission to the data port enables the environment for real-time healthcare services. We propose a novel method for epileptic seizure prediction based on supervised machine-learning techniques. The method consists of simple signal preprocessing with only three feature extraction, and a random-forest classifier that recognizes the change in brain activity heralding the seizures (preictal state). The proposed method fulfilled the challenges of the pervasive healthcare system such as robustness to the presence of the artifact, stationarity of time series, and execution speed. The proposed method is verified on publicly available CHB-MIT EEG datasets recorded on pediatric subjects. The achieved averaged sensitivity and specificity are 92.62 % and 90.8 %, respectively. The proposed method is compared to eight state-of-art methods designed for clinical applications, with similar performance in one case, outperforming the remaining seven.

关键词： scalp EEG machine-learning algorithms healthcare pervasive environments epileptic seizures prediction

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Implementing indirect test of RF circuits without compromising test quality: a practical case study 21

Implementing indirect test of RF circuits without compromisi...

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21st IEEE Latin-American Test Symposium (LATS)

作者： El Badawi, H. Azais, F. Bernard, S. Comte, M. Kerzerho, V Lefevre, F. Gorenflot, I Univ Montpellier CNRS LIRMM 161 Rue Ada F-34095 Montpellier France NXP Semicond 2 Esplanade Anton Philips F-14000 Caen France

ISBN: (纸本)9781728187310

Cost reduction is a crucial step in testing AMS/RF circuits, and one way of achieving this is to implement an indirect test strategy. Although this strategy relaxes the requirements on test equipment, it still raises some accuracy concerns, which might compromise the test quality. In this work, we explore the benefit that can be brought by a two-tier adaptive test flow, in which only circuits with a sufficient prediction confidence level are evaluated by the indirect test while others are re-evaluated by specification based test. A methodology is presented that permits to explore different tradeoffs between test quality and test cost and to make pertinent choices for the efficient implementation of such a test flow. The results are illustrated on a front-end RF circuit designed for WLAN applications and show that substantial test cost reduction can be achieved without compromising the test quality.

关键词： Indirect test RF integrated circuits machine-learning algorithms quality metrics test efficiency test confidence

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Detection of vibrating objects in SAR images 23

Detection of vibrating objects in SAR images

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Conference on Radar Sensor Technology XXIII

作者： Perez, Francisco Santhanam, Balu Atwood, Thomas Dunkel, Ralf Doerry, Armin W. Hayat, Majeed M. Univ New Mexico Ctr High Technol Mat 1313 Goddard SE Albuquerque NM 87106 USA Univ New Mexico Elect & Comp Engn Dept 1313 Goddard SE Albuquerque NM 87106 USA Gen Atom Aeronaut Syst Inc 14200 Kirkham Way Poway CA 92121 USA Sandia Natl Labs 1515 Eubank SE Albuquerque NM 87123 USA Marquette Univ Dept Elect & Comp Engn 1515 W Wisconsin Ave Milwaukee WI 53233 USA

ISBN: (纸本)9781510626720

The vibratory response of buildings and machines carries key information that can be exploited to infer their operating conditions and to diagnose failures. Moreover, since vibration signatures observed from the exterior surfaces of structures are intrinsically linked to the type of machinery operating inside of them, the ability to monitor vibrations remotely can facilitate the detection and identification of the machinery. Recently, synthetic aperture radar (SAR) has proven to be a versatile tool capable of performing vibrometry and high-precision vibration-estimation algorithms have been developed for reconstructing surface vibration waveforms from SAR images. However, these algorithms tend to be computationally demanding and, in addition, require knowledge of the exact location of the object a priori. This renders their use as unpractical for exploratory applications. This paper focuses on the detection of vibrating objects by exploiting the phase modulation that a vibration causes in the received slow-time SAR data. Two different vibration detection schemes are investigated. The first scheme is data-driven and utilizes features extracted with the help of the discrete fractional Fourier transform (DFrFT) to feed a random-forest detector. The second scheme is model-based, and uses a probabilistic model of the slow-time SAR signal, the Karhunen-Loeve expansion, and a likelihood-ratio detector. The proposed detection algorithms are tested using both simulated and real SAR data. Our results show that both detection schemes can be used to achieve high-performance vibrating-object detectors.

关键词： Synthetic aperture radar vibration detection discrete fractional Fourier transform time-frequency analysis Karhunen-Loeve expansion machine-learning algorithms

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Translator Data Pre-processing Gram Feature Algorithmic Model (TDGA) for Enhancing Classifier Accuracy in the Healthcare Domain

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SN Computer Science 2023年第5期4卷 542页

作者： Sathya, A. Mythili, M.S. Department of Computer Science Bishop Heber College (Autonomous) (Affiliated to Bharathidasan University) Tamil Nadu Trichy 620017 India Department of Computer Applications Bishop Heber College (Autonomous) (Affiliated to Bharathidasan University) Tamil Nadu Trichy 620017 India

Coronavirus COVID-19 has been spreading like wildfire all over the world since the year 2019. Nowadays, everyone is interacting with social media on a regular basis. In this study, the primary objective is to examine how Twitter users feel about COVID-19’s social life and to conclude their opinions. The polarity of feelings is determined by machine-learning classifiers that use popular words such as Coronavirus and COVID-19 to identify them. In addition, viruses such as BA-4 and BA-5 types of omicrons are spreading widely all over the globe. In order to prevent themselves from these types of viruses, the public needs to know the exact sentiment of the current social life problem. Using the newly reported topics/themes/issues and the associated sentiments from various factors, the COVID-19 pandemic can be better understood. A large dataset of tweets conveying information regarding COVID-19 is analyzed in this article, in particular, the credibility of the information shared on Twitter. It was evaluated using unigrams, bigrams, and trigrams with different parameters such as f1 score, precision, recall and compared against accuracy using machine-learning techniques such as Support Vector machine (SVM), Naive Bayes (NB), and Logistic Regression (LR). The model TDGA (Translator Data pre-processing Gram feature Algorithmic model) performs well in individuals’ assessments of COVID-19 and benchmark COVID datasets, with a maximum efficiency of 86%. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Language translation method machine-learning algorithms Pre-processing techniques Sentiment analysis Social media

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