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An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction

MEDICAL ENGINEERING & PHYSICS 2024年 130卷 104213页

作者： Zhu, Lei Wang, Wentao Huang, Aiai Ying, Nanjiao Xu, Ping Zhang, Jianhai Hangzhou Dianzi Univ Sch Automat Hangzhou 310000 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310000 Peoples R China Key Lab Brain Machine Collaborat Intelligence Zhej Hangzhou 310018 Peoples R China

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effectively improve the quality of life for patients with the condition. While there are many methods for detecting epilepsy, EEG is currently considered one of the most effective analytical tools due to the abundant information it provides about brain activity. The aim of this study is to explore potential time-frequency and channel features from multi-channel epileptic EEG signals and to develop a patientspecific seizure prediction network. In this paper, an epilepsy EEG signal classification algorithm called Channel Recurrent Criss-cross Attention Network (CRCANet) is proposed. Firstly, the spectrograms processed by the short-time fourier transform is input into a Convolutional Neural Network (CNN). Then, the spectrogram feature map obtained in the previous step is input into the channel attention module to establish correlations between channels. Subsequently, the feature diagram containing channel attention characteristics is input into the recurrent criss-cross attention module to enhance the information content of each pixel. Finally, two fully connected layers are used for classification. We validated the method on 13 patients in the public CHB-MIT scalp EEG dataset, achieving an average accuracy of 93.8 %, sensitivity of 94.3 %, and specificity of 93.5 %. The experimental results indicate that CRCANet can effectively capture the time-frequency and channel characteristics of EEG signals while improving training efficiency.

关键词： EEG Epilepsy prediction classification algorithm Spectrogram Short-time fourier transform Attention

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EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts

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BMC BIOINFORMATICS 2009年第Sup8期10卷 S2-S2页

作者： Yeniterzi, Sueveyda Sezerman, U. Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey

Background: A better understanding of the mechanisms of an enzyme's functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes' databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme. Results: We present an automated system which identifies the abstracts that contain an amino-acid-level mutation and then classifies them according to the mutation's effect on the enzyme. In the case of mutation identification, MuGeX, an automated mutation-gene extraction system has an accuracy of 93.1% with a 91.5 F-measure. For impact analysis, document classification is performed to identify the abstracts that contain a change in enzyme's stability or activity resulting from the mutation. The system was trained on lipases and tested on amylases with an accuracy of 85%. Conclusion: EnzyMiner identifies the abstracts that contain a protein mutation for a given enzyme and checks whether the abstract is related to a disease with the help of information extraction and machine learning techniques. For disease related abstracts, the mutation list and direct links to the abstracts are retrieved from the system and displayed on the Web. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme's stability or functionality followed by displaying th

关键词： Lipase Amylase Protein Mutation classification algorithm Regular Expression

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Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

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ENERGIES 2021年第20期14卷 6677页

作者： Koteleva, N. I. Korolev, N. A. Zhukovskiy, Y. L. St Petersburg Min Univ Educ Res Ctr Digital Technol St Petersburg 191106 Russia

The paper discusses the method of identifying the technical condition of induction motors by classifying the energy data coming from the main common power bus. The work shows the simulation results of induction motor operation. The correlation between occurring defects and current diagrams is presented. The developed simulation model is demonstrated. The general algorithm for conducting experiments is described. Five different experiments to develop an algorithm for the classification are conducted: determination of the motors number in operation with different power;determination of the motors number in operation with equal power;determination of the mode and load of induction electric motor;determination of the fault and its magnitude with regard to operation and load of induction motor;determination of the fault and its magnitude with regard to operation and load of induction motor with regard to non-linear load in the flow. The article also presents an algorithm for preprocessing data to solve the classification problem. In addition, the classification results are shown and recommendations for testing and using the classification algorithm on a real object are made.

关键词： classification algorithm induction electric motor simulation model the coefficient of electromagnetic momentum ripple current harmonic distortion factor

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Inferring ethnicity from mitochondrial DNA sequence

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BMC proceedings 2011年第2-Sup期5 Suppl 2卷 S11页

作者： Chih Lee Ion I Măndoiu Craig E Nelson Computer Science and Engineering Department University of Connecticut Storrs CT USA. chihlee@engr.uconn.edu.

BACKGROUND:The assignment of DNA samples to coarse population groups can be a useful but difficult task. One such example is the inference of coarse ethnic groupings for forensic applications. Ethnicity plays an important role in forensic investigation and can be inferred with the help of genetic markers. Being maternally inherited, of high copy number, and robust persistence in degraded samples, mitochondrial DNA may be useful for inferring coarse ethnicity. In this study, we compare the performance of methods for inferring ethnicity from the sequence of the hypervariable region of the mitochondrial genome. RESULTS:We present the results of comprehensive experiments conducted on datasets extracted from the mtDNA population database, showing that ethnicity inference based on support vector machines (SVM) achieves an overall accuracy of 80-90%, consistently outperforming nearest neighbor and discriminant analysis methods previously proposed in the literature. We also evaluate methods of handling missing data and characterize the most informative segments of the hypervariable region of the mitochondrial genome. CONCLUSIONS:Support vector machines can be used to infer coarse ethnicity from a small region of mitochondrial DNA sequence with surprisingly high accuracy. In the presence of missing data, utilizing only the regions common to the training sequences and a test sequence proves to be the best strategy. Given these results, SVM algorithms are likely to also be useful in other DNA sequence classification applications.

关键词： Support Vector Machine Linear Discriminant Analysis classification algorithm Hypervariable Region Published Dataset

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Designing a Model to Study Data Mining in Distributed Environment

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Journal of Data Analysis and Information Processing 2021年第1期9卷 23-29页

作者： Md. Abadur Rahman Masud Karim School of Business & Tourism Southern Cross University Sydney Australia Department of Computer Science & Engineering Daffodil Institute of Science & Technology Dhaka Bangladesh

To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using data mining to perform such tasks. Data mining techniques are used to find hidden information from large data source. Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach. Data is becoming larger and spreading geographically. So it is difficult to find better result from only a central data source. For knowledge discovery, we have to work with distributed database. On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data. For this reason, distributed database is essential for future processing. In this paper, we have proposed a framework to study data mining in distributed environment. The paper presents a framework to bring out actionable knowledge. We have shown some level by which we can generate actionable knowledge. Possible tools and technique for these levels are discussed.

关键词： Data Mining Distributed Database Knowledge Discovery classification algorithm

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Network embedding framework for driver gene discovery by combining functional and structural information

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BMC GENOMICS 2023年第1期24卷 426-426页

作者： Chu, Xin Guan, Boxin Dai, Lingyun Liu, Jin-xing Li, Feng Shang, Junliang Qufu Normal Univ Sch Comp Sci Rizhao 27826 Peoples R China

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

关键词： Driver gene Gene interaction network Network embedding Mutation data classification algorithm

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Machine learning-based processing of unbalanced data sets for computer algorithms

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OPEN COMPUTER SCIENCE 2023年第1期13卷

作者： Zhou, Qingwei Qi, Yongjun Tang, Hailin Wu, Peng Guangdong Baiyun Univ Fac Megadata & Comp Guangzhou 510450 Guangdong Peoples R China Mongolian Univ Sci & Technol Sch Informat & Commun Technol Ulan Bator 13341 Mongolia Sichuan Tourism Univ Sch Informat & Engn Chengdu 610000 Sichuan Peoples R China

The rapid development of technology allows people to obtain a large amount of data, which contains important information and various noises. How to obtain useful knowledge from data is the most important thing at this stage of machine learning (ML). The problem of unbalanced classification is currently an important topic in the field of data mining and ML. At present, this problem has attracted more and more attention and is a relatively new challenge for academia and industry. The problem of unbalanced classification involves classifying data when there is insufficient data or severe category distribution deviations. Due to the inherent complexity of unbalanced data sets, more new algorithms and tools are needed to effectively convert a large amount of raw data into useful information and knowledge. Unbalanced data set is a special case of classification problem, in which the distribution between classes is uneven, and it is difficult to classify data accurately. This article mainly introduces the research on the processing method of computer algorithms based on the processing method of unbalanced data sets based on ML, aiming to provide some ideas and directions for the processing of computer algorithms based on unbalanced data sets based on ML. This article proposes a research strategy for processing unbalanced data sets based on ML, including data preprocessing, decision tree data classification algorithm, and C4.5 algorithm, which are used to conduct research experiments on processing methods for unbalanced data sets based on ML. The experimental results in this article show that the accuracy rate of the decision tree C4.5 algorithm based on ML is 94.80%, which can be better used for processing unbalanced data sets based on ML.

关键词： machine learning unbalanced data sets classification algorithm data processing computer algorithm decision tree classification

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Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients

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PATHOLOGY RESEARCH AND PRACTICE 2023年 242卷 154311页

作者： Jeyananthan, Pratheeba Univ Jaffna Fac Engn Jaffna Sri Lanka

SARS-CoV-2 pandemic is the current threat of the world with enormous number of deceases. As most of the countries have constraints on resources, particularly for intensive care and oxygen, severity prediction with high accuracy is crucial. This prediction will help the medical society in the selection of patients with the need for these constrained resources. Literature shows that using clinical data in this study is the common trend and molecular data is rarely utilized in this prediction. As molecular data carry more disease related information, in this study, three different types of RNA molecules (lncRNA, miRNA and mRNA) of SARS-COV-2 patients are used to predict the severity stage and treatment stage of those patients. Using seven different machine learning al-gorithms along with several feature selection techniques shows that in both phenotypes, feature importance selected features provides the best accuracy along with random forest classifier. Further to this, it shows that in the severity stage prediction miRNA and lncRNA give the best performance, and lncRNA data gives the best in treatment stage prediction. As most of the studies related to molecular data uses mRNA data, this is an interesting finding.

关键词： Severity prediction classification algorithm lncRNA miRNA and mRNA COVID-19 molecular data Feature selection Treatment stage

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A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing

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Procedia Computer Science 2021年 182卷 13-20页

作者： Muhammad Aminu Lawal Riaz Ahmed Shaikh Syed Raheel Hassan Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks.

关键词： Internet of Things (IoT) Anomaly mitigation DDoS Fog computing classification algorithm

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On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection

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SENSORS 2016年第12期16卷 2051-2051页

作者： Manfredini, Esteban Garbin Dovis, Fabio Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy

In this paper, the Extended Coupled Amplitude Delay Lock Loop (ECADLL) architecture, previously introduced as a solution able to deal with a multipath environment, is revisited and improved to tailor it to spoofing detection purposes. Exploiting a properly-defined decision algorithm, the architecture is able to effectively detect a spoofer attack, as well as distinguish it from other kinds of interference events. The new algorithm is used to classify them according to their characteristics. We also introduce the use of a ratio metric detector in order to reduce the detection latency and the computational load of the architecture.

关键词： signal processing GNSS receiver spoofing detection classification algorithm

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