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Machine Learning Methods of Symptom Extraction from Medical Notes

Machine Learning Methods of Symptom Extraction from Medical ...

引用

2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Galai, Olesia Kurdiumov, Dmitrii Rusak, Alena ITMO University Faculty of Software Engineering and Computer Systems Saint-Petersburg Russia

ISBN: (纸本)9798350395044

Medical notes contain valuable information about patient conditions, treatments, and progress. Extracting symptoms from these unstructured notes is crucial for clinical research, population health analysis, and decision support systems. Traditional manual methods are time-consuming, but recent advances in natural language processing (NLP) and machine learning offer automated solutions. This article presents a novel approach that combines NLP techniques, such as conditional random fields (CRF) and transformer-based architectures. The proposed method demonstrates effective symptom extraction from medical notes, overcoming challenges such as varied terminologies and linguistic nuances. The study utilizes a dataset of Russian medical records, transforming it into a tabular format for training and employing unique tokenization algorithms for different models. Among the evaluated models, RuBERT achieved the highest accuracy of 91%, indicating its strong performance on the test dataset. SBERT exhibited the highest precision and F1 score, suggesting its effectiveness in accurately identifying specific sequence labels. © 2024 IEEE.

关键词： Clinical research

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment 7

Application of Artificial Intelligence Methods for Generatin...

引用

7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Salavatovich, Nazipov Rustam Yurievna, Avksentieva Elena ITMO University Faculty of Software Engineering and Computer System Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer System Saint-Petersburg Russia

ISBN: (纸本)9798350354973

The present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. The research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. The methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. The study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context. © 2024 IEEE.

关键词： Neural network models

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HAMI: Hybrid Adaptive AutoML for IoT Intrusion Detection

HAMI: Hybrid Adaptive AutoML for IoT Intrusion Detection

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2025 Communication Strategies in Digital Society Seminar, ComSDS 2025

作者： Saber, Mohamed Gusarova, Natalia Bogatyrev, Vladimir Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

ISBN: (纸本)9798331536121

the growth of IoT devices presents significant security challenges due to their diverse and dynamic environments. To address this, we developed HAMI, a novel intrusion detection system. Our method leverages the GIFS algorithm, which combines feature rankings from ensemble models with genetic algorithms to uncover subtle inter-feature correlations, enhancing detection accuracy. Additionally, we introduce GridSearchSMOTE, a technique that optimizes data balancing by evaluating various SMOTE variants through a meticulous grid search, ensuring optimal performance for each dataset. Empirical evaluations of HAMI show superior accuracy, achieving a 99% detection rate and reduced false positives, outperforming existing systems. These results underscore HAMI's potential to significantly improve IoT network security by exploring its scalability and real-time detection capabilities. © 2025 IEEE.

关键词： Network intrusion

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Building a Full-text Search Index Using "Transformer" Neural Network 17

Building a Full-text Search Index Using "Transformer" Neural...

引用

17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Dobrynin, Vyacheslav Abramovich, Roman Platonov, Alexey Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9798350303568

The use of deep neural networks in information retrieval significantly improves its effectiveness, but negatively affects the performance of the process. To deal with this, we propose a new ranking model that uses the deep neural network of the "Transformer"architecture (in particular, BERT) for efficient information retrieval. In accordance with the proposed approach, contextualized vector representations are extracted from documents during indexing, after which these representations are clustered for each independent token. The resulting clusters reflect different meanings of the words and are indirectly used as inverted index keys. The values represent the documents in which these contextualized word meanings occur, along with the distances from each document to the contextualized embedding. Thus, after the indexing process, we obtain an index containing pre-calculated distances between the contextualized meanings of dictionary elements and documents. This approach helps us avoid the performance overhead of calculating distances online. At the search stage, the query is transformed into a set of contextualized vectors representing each query token, which allows us to use these vectors to retrieve most semantically close neighbor-tokens and use them to extract relevant documents from the index. This way of searching for contextualized embeddings consumes less memory and is more performant due to the use of an inverted index. © 2023 IEEE.

关键词： Search engines

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Priority Service in Data Transmission Networks with Non-Stationary Flows

Priority Service in Data Transmission Networks with Non-Stat...

引用

2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Lygdenov, Sodnom ITMO University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

ISBN: (纸本)9798350395044

The subject of research. Analysis of the impact of priority data transmission in high-load multi-channel systems represented as a mass service system with request prioritization under different variations in the distribution of random variables of packet arrival intensity in the network and service time in the channels. Method. The study of the influence of priorities on the efficiency of data transmission in multi-channel systems was carried out using simulation and mathematical statistics methods. Main results. The method of numerical calculation of time parameters based on the simulation model of multi-channel data transmission system with heterogeneous load is proposed. Simulation experiments evaluating the impact of priority data transmission at different coefficients of variation of packet intensity and service time in the channels on the time characteristics of the multi-channel system are conducted. Practical significance. The obtained results can be applied to the design of multi-channel computer systems of high load data transmission, providing the optimal use of priorities to improve the efficiency of data transmission. © 2024 IEEE.

关键词： Random variables

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Efficiency in Multi-Channel Data Transmission systems with Priority Service Under High Load

Efficiency in Multi-Channel Data Transmission Systems with P...

引用

2024 International Russian Automation Conference, RusAutoCon 2024

作者： Lygdenov, Sodnom Itmo University Faculty of Software Engineering and Computer Engineering Saint Petersburg Russia

The subject of research. Analysis of the influence of priority service in multi-channel data transmission systems characterized by limited storage capacity and high load, taking into account the non-stationary characteristics of the intensity of packet arrivals and service time. Method. The functional characteristics of the multi-channel system were calculated and analyzed using simulation methods and mathematical statistics. Main results. A simulation model of a multi-channel system with priority service has been developed, designed to evaluate the functional characteristics of the system under high load. Experiments have been conducted to study the impact of priority service on delays and packet losses of heterogeneous traffic. Practical significance. The results obtained can be used in the design of multi-threaded data transmission systems operating under conditions of high heterogeneous load. © 2024 IEEE.

关键词： data transmission networks limited-capacity storage devices multi-channel systems network efficiency priority servicing of packets queuing systems simulation experiments

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

引用

27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the university of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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Russian Language Datasets in the Digital Humanities Domain and Their Evaluation with Word Embeddings 1

引用

20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Wohlgenannt, Gerhard Babushkin, Artemii Romashov, Denis Ukrainets, Igor Maskaykin, Anton Shutov, Ilya Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia

ISBN: (数字)9783031243370

ISBN: (纸本)9783031243363

In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learning algorithms. The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The characteristics of the tasks and datasets are that they build upon small, domain-specific corpora, and that the datasets contain a high number of named entities. The datasets were created manually for two fantasy novel book series ("A Song of Ice and Fire" and "Harry Potter"). We provide baseline evaluations with popular word embedding models trained on the book corpora for the given tasks, both for the Russian and English language versions of the datasets. Finally, we compare and analyze the results and discuss specifics of Russian language with regards to the problem setting. © 2023, Springer Nature Switzerland AG.

关键词： Embeddings

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A Spectral Mask - Based on Method for Applying into Generalized Sidelobe Canceller Beamformer 12th

A Spectral Mask - Based on Method for Applying into Gener...

引用

Proceedings of the12th International Conference on Information Technology and its Applications, CITA 2023

作者： Huy, Nguyen Ba Anh, Pham Tuan The, Quan Trong Faculty of Control Systems and Robotics University ITMO St.Petersburg Russia Faculty of Software Engineering and Computer Systems University ITMO St.Petersburg Russia

ISBN: (纸本)9783031368851

The problem of signal processing is extracting the desired target speaker while suppressing the annoying background noise in complex recording situation, where exists third-party talker, transport vehicle or interference, noise. Speech quality or speech intelligibility are the major characteristics, which confirm the effectiveness of processed method. In speech applications, such as hearing-aids, hands-free telephony, teleconferencing, acoustic instruments, microphone array (MA) is applied for increasing the speech quality. MA uses the spatial response, the priori information about direction - of - arrival (DOA) of interest signal, the properties of recording environment to obtain speech components and remove noise. Generalized Sidelobe Canceller (GSC) is one of the most widely exploited in microphone array beamforming, due to its convenience and effective noise reduction. However, in several situations, the performance of GSC beamformer is often degraded, because of the imprecise DOA. In this paper, the authors suggested an additive spectral mask for improving the evaluation of GSC beamformer. The resulting experiment proves the received advantage of the author’s proposed algorithm to increase in the term of the signal-to-noise ratio (SNR). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Signal to noise ratio

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Neural processor microarchitecture design for CNN processing acceleration 11

Neural processor microarchitecture design for CNN processing...

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Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Tabunshchik, Sergei Bykovskii, Sergei Kustarev, Pavel Faculty of Software Engineering and Computer Systems ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

ISBN: (纸本)9781510682061

Mixed reality systems that integrate the real and digital worlds have recently gained popularity. The development of mixed reality devices in the form of wearable devices has been a significant trend in recent years. Key challenges in designing wearable mixed reality devices include reducing power consumption, enhancing productivity, and minimizing the size of the device. To address these challenges, neural processors are being incorporated into mixed reality systems. These are specialized hardware accelerators for convolutional neural network (CNN) processing. The use of neural processors is due to the fact that most environmental analysis and visualizations in mixed reality rely on CNNs. The use of these energy-efficient, high-performance processing devices enables increased comfort in using the wearable device through the high-performance processing of environmental data on a compact device. The paper is devoted to the design of the microarchitecture of a neural processor for hardware acceleration of CNN processing based on the author's architecture of processor. The paper presents various microarchitectural solutions that can be used to accelerate CNN processing. We explore methods to optimize hardware resources and reduce time required for CNN processing. To achieve high throughput in pipelined computation, different algorithms for convolution calculations in a systolic array are examined. Based on the results of this research, we provide estimates of the characteristics of the neural processor with the proposed microarchitecture. © 2024 SPIE.

关键词： Convolutional neural networks

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