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A comparison study on algorithms of detecting long forms for short forms in biomedical text

A comparison study on algorithms of detecting long forms for...

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1st international workshop on Text mining in Bioinformatics

作者： Torii, Manabu Hu, Zhang-zhi Song, Min Wu, Cathy H. Liu, Hongfang Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20057 USA Georgetown Univ Med Ctr Dept Biochem & Mol & Cell Biol Washington DC 20007 USA Univ Hts New Jersey Inst Technol Dept Informat Syst Newark NJ 07102 USA

Motivation: With more and more research dedicated to literature mining in the biomedical domain, more and more systems are available for people to choose from when building literature mining applications. In this study, we focus on one specific kind of literature mining task, i.e., detecting definitions of acronyms, abbreviations, and symbols in biomedical text. We denote acronyms, abbreviations, and symbols as short forms (SFs) and their corresponding definitions as long forms (LFs). The study was designed to answer the following questions;i) how well a system performs in detecting LFs from novel text, ii) what the coverage is for various terminological knowledge bases in including SFs as synonyms of their LFs, and iii) how to combine results from various SF knowledge bases. Method: We evaluated the following three publicly available detection systems in detecting LFs for SFs: i) a handcrafted pattern/rule based system by Ao and Takagi, ALICE, ii) a machine learning system by Chang et al., and iii) a simple alignment-based program by Schwartz and Hearst. In addition, we investigated the conceptual coverage of two terminological knowledge bases: i) the UMLS (the Unified Medical Language System), and ii) the BioThesaurus (a thesaurus of names for all UniProt protein records). We also implemented a web interface that provides a virtual integration of various SF knowledge bases. Results: We found that detection systems agree with each other on most cases, and the existing terminological knowledge bases have a good coverage of synonymous relationship for frequently defined LFs. The web interface allows people to detect SF definitions from text and to search several SF knowledge bases.

关键词： learning systems

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NCBI: A Novel Correlation Based Imputing Technique Using Biclustering 1st

NCBI: A Novel Correlation Based Imputing Technique Using Bic...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Chowdhury, Hussain A. Ahmed, Hasin A. Bhattacharyya, Dhruba Kumar Kalita, Jugal K. Computer Science and Engineering Tezpur University SonitpurAssam784028 India Department of Information and Computer Science Assam Women’s University JorhatAssam785004 India Computer Science University of Colorado Colorado SpringsCO80933-7150 United States

ISBN: (纸本)9789811390418

Presence of missing values (MV) in gene expression data is commonplace. It significantly affects the performance of statistical analysis and machine learning algorithms. Discarding objects or attributes with missing values and inappropriate estimation of MVs lead to high information loss and misleading results. So, it is necessary to have an accurate technique for missing value imputation. In this paper, we present a novel correlation based missing value imputation technique for gene expression datasets. We refer to our method as NCBI. We compare the estimation accuracy of our technique with two widely used methods such as KNNI and KMI, on four benchmark datasets by randomly knocking out data values as missing. Our technique can estimate missing values almost 20–25% more accurately than KNNI and KMI in all datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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1st international Conference on Advanced Informatics for Computing Research, ICAICR 2017

1st International Conference on Advanced Informatics for Com...

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1st international Conference on Advanced Informatics for Computing Research, ICAICR 2017

ISBN: (纸本)9789811057793

The proceedings contain 32 papers. The special focus in this conference is on Advanced Informatics for Computing Research. The topics include: Fuzzy based efficient mechanism for URL assignment in dynamic web crawler;towards filtering of SMS spam messages using machine learning based technique;intelligent computing methods in language processing by brain;classification algorithms for prediction of lumbar spine pathologies;keyword based identification of thrust area using mapreduce for knowledge discovery;an efficient genetic algorithm for fuzzy community detection in social network;priority based service broker policy for fog computing environment;software remodularization by estimating structural and conceptual relations among classes and using hierarchical clustering;requirements traceability through information retrieval using dynamic integration of structural and co-change coupling;bilingual code-mixing in Indian social media texts for Hindi and English;performance evaluation and comparative study of color image segmentation algorithm;electroencephalography based analysis of emotions among Indian film viewers;fuel assembly height measurements at the nuclear power plant unit active zone;a novel approach to segment nucleus of uterine cervix pap smear cells using watershed segmentation;parametric study of various direction of arrival estimation techniques;deep CNN-based method for segmenting lung fields in digital chest radiographs;quality assessment of a job portal system designed using bout design pattern;analyzing factors affecting the performance of data mining tools;stable feature selection with privacy preserving data mining algorithm and an efficient routing protocol for DTN.

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Sub-scene Target Detection and recognition Using Deep learning Convolution Neural Networks 1st

Sub-scene Target Detection and Recognition Using Deep Learni...

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1st international Conference on data Science, machine learning and Applications, 2019

作者： Merugu, Suresh Jain, Kamal Mittal, Anshul Raman, Balasubramanian R&D Centre CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9789811514197

Sub-scene recognition algorithm based on super resolution along with scene dependent neural network model and sub-scene dependent target detection for automating object information extraction is proposed. This work deals with large number of challenges possessed by classification problems. Some of the challenges in this problem are the low resolution satellite images, diverse pattern of each sub-scene causing the low level learning for classification and plethora of distinct object classes present in each sub-scene causes low accuracy of object detection. Objective of this paper presents an image super resolution technique for rectifying problems posed by low resolution images with color density variations of chromaticity coordinates. To eliminate the problem of diverse patterns, have divided various land cover types into separate groups based on maximum mixed fraction among these groups and corresponding sub-scene recognition disparate model parameters are used to recognize various scenes. To increase the accuracy for object detection has developed a sub-scene dependent Neural Network model for extracting the target/anomaly of object information. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st international Iranian Conference on Biomedical Engineering, ICBME 2016

2016 23rd Iranian Conference on Biomedical Engineering and 2...

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23rd Iranian Conference on Biomedical Engineering and 2016 1st international Iranian Conference on Biomedical Engineering, ICBME 2016

ISBN: (纸本)9781509034529

The proceedings contain 66 papers. The topics discussed include: performance evaluation of dopamine electrochemical sensor in presence of uric acid, ascorbic acid and serotonin as interferer agents;an instrumented electromechanical apparatus for mechanical characterization of human hand palm soft tissue;the effects of pre-indenting load on mechanical response of bovine meniscus;structural connectivity of temporal lobe structures detects temporal lobe epilepsy;classification of Alzheimer's disease and mild cognitive impairment: machine learning applied to rsfMRI brain graphs;network based analysis of cognitive related resting state networks in Alzheimer's disease;improvement of flexible design matrix in sparse Bayesian learning for multi task fMRI data analysis;brain computer interface design and implementation to identify overt and covert speech;effective connectivity measuring of ERP signals in recognition memory process by generalized partial directed coherence;analysis of brain connectivity patterns in autistic children during watching emotional faces;spatial filter bank based on probabilistic common spatial pattern in motor imagery BCI systems;a low cost, efficient method for chip fabrication in cell analysis;a novel microfluidic design for blood plasma separation;towards smart ehealth in the ultra large-scale Internet of Things era;estimation of brain tumour volume using expanded computed tomography scan images;retinal optic disk segmentation and analysis in fundus images using DBSCAN clustering algorithm;and automatic zone identification in blood smear images using optimal set of features.

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Co-transfer learning via joint transition probability graph based method

Co-transfer learning via joint transition probability graph ...

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1st ACM SIGKDD workshop on Cross Domain Knowledge Discovery in Web and Social Network mining 2012, CDKD 2012

作者： Ng, Michael K. Wu, Qingyao Ye, Yunming Department of Mathematics Hong Kong Baptist University Kowloon Tong Hong Kong Department of Computer Science Harbin Institute of Technology Shenzhen Graduate School China

ISBN: (纸本)9781450315555

This paper studies a new machine learning strategy called co-transfer learning. Unlike many previous learning problems, we focus on how to use labeled data of different feature spaces to enhance the classification of different learning spaces simultaneously. For instance, we make use of both labeled images and labeled text data to help learn models for classifying image data and text data together. An important component of co-transfer learning is to build different relations to link different feature spaces, thus knowledge can be co-transferred across different spaces. Our idea is to model the problem as a joint transition probability graph. The transition probabilities can be constructed by using the intra-relationships based on affinity metric among instances and the inter-relationships based on co-occurrence information among instances from different spaces. The proposed algorithm computes ranking of labels to indicate the importance of a set of labels to an instance by propagating the ranking score of labeled instances via the random walk with restart. The main contribution of this paper is to (i) propose a co-transfer learning (CT-Learn) framework that can perform learning simultaneously by co-transferring knowledge across different spaces;(ii) show the theoretical properties of the random walk for such joint transition probability graph so that the proposed learning model can be used effectively;(iii) develop an efficient algorithm to compute ranking scores and generate the possible labels for a given instance. Experimental results on benchmark data (image-text and English-Chinese-French classification data sets) have shown that the proposed algorithm is computationally efficient, and effective in learning across different spaces. In the comparison, we find that the classification performance of the CT-Learn algorithm is better than those of the other tested transfer learning algorithms. Copyright 2012 ACM.

关键词： Iterative methods

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Event2vec: learning Representations of Events on Temporal Sequences 1st

Event2vec: Learning Representations of Events on Temporal Se...

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1st Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint international Conference on Web and Big data

作者： Hong, Shenda Wu, Meng Li, Hongyan Wu, Zhengwu Minist Educ Key Lab Machine Percept Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Control & Elect Technol Sci & Technol Informat Syst Engn Lab Beijing Peoples R China

ISBN: (纸本)9783319635644;9783319635637

Sequential data containing series of events with timestamps is commonly used to record status of things in all aspects of life, and is referred to as temporal event sequences. learning vector representations is a fundamental task of temporal event sequence mining as it is inevitable for further analysis. Temporal event sequences differ from symbol sequences and numerical time series in that each entry is along with a corresponding time stamp and that the entries are usually sparse in time. Therefore, methods either on symbolic sequences such as word2vec, or on numerical time series such as pattern discovery perform unsatisfactorily. In this paper, we propose an algorithm called event2vec that solves these problems. We first present Event Connection Graph to summarize events while taking time into consideration. Then, we conducts a training Sample Generator to get clean and endless data. Finally, we feed these data to embedding neural network to get learned vectors. Experiments on real temporal event sequence data in medical area demonstrate the effectiveness and efficiency of the proposed method. The procedure is totally unsupervised without the help of expert knowledge. Thus can be used to improve the quality of health-care without any additional burden.

关键词： Temporal event sequences learning representations

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A method for detecting botnets in IT infrastructure using a neural network 1

A method for detecting botnets in IT infrastructure using a ...

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1st international workshop on Intelligent and CyberPhysical Systems, ICyberPhyS 2024

作者： Denysiuk, Dmytro Sochor, Tomas Kapustian, Mariia Kashtalian, Antonina Drozd, Andriy Khmelnytskyi National University Institutska str. 11 Khmelnytskyi29016 Ukraine Prigo University College European Research University Vítězslava Nezvala 801/1 Union Havířov736 01 Czech Republic

Information technology has become an integral part of modern life, but with this come new cyber threats. One of them is botnets—networks of infected computers that criminals use for DDoS attacks, data theft, and spam distribution. Traditional detection methods, such as signature analysis and rule-based approaches, often fail to handle these threats, necessitating the implementation of advanced methods. This article presents a botnet detection method in IT infrastructure based on the use of neural networks. The proposed approach involves creating a baseline configuration of the IT infrastructure by a system administrator for further training of neural networks to detect botnet attacks. Experiments conducted on four types of botnets (DDoS, spam, data theft, and cryptocurrency mining) demonstrated high accuracy and efficiency of the system. The method achieved 96% accuracy in detecting DDoS attacks, 93% in detecting spam botnets, 95% in detecting data theft botnets, and 94% in detecting cryptocurrency mining botnets. The use of a genetic algorithm for training neural networks improved detection efficiency. The method demonstrates high detection speed, with an average time of less than one second. Thus, the developed method is an effective tool for ensuring the security of IT infrastructure, confirming the relevance of using neural networks and machine learning for cybersecurity. Further research is aimed at improving the adaptability of neural networks and reducing the computational resources required for model parameter optimization. © 2023 Copyright for this paper by its authors.

关键词： Anomaly detection

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Gait recognition with Smart Devices Assisting Postoperative Rehabilitation in a Clinical Setting 1

Gait Recognition with Smart Devices Assisting Postoperative ...

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1st IEEE international Conference on Artificial Intelligence for Industries (AI4I)

作者： Kyritsis, Athanasios I. Willems, Geoffrey Deriaz, Michel Konstantas, Dimitri Univ Geneva GSEM CUI Informat Sci Inst Geneva Switzerland Hirslanden Clin La Colline Physiotherapy Ctr Geneva Switzerland

ISBN: (纸本)9781538692097

Postoperative rehabilitation is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. This kind of rehabilitation is led by physiotherapists who assess each situation and prescribe appropriate exercises. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions, and therefore literature on this topic is still in its infancy. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns including walking with crutches with various levels of weight-bearing, walking with different frames, limping and walking normally. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. A gait detection accuracy of 94.9% was achieved among nine different gait classes, as these were labeled by professional physiotherapists.

关键词： Activity recognition feature extraction machine learning pattern recognition smart devices wearable computers wearable sensors

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Advances in Computational Intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Joan Cabestany Ignacio Rojas Gonzalo Joya

ISBN: (数字)9783642214981

ISBN: (纸本)9783642214974

This two-volume set LNCS 6691 and 6692 constitutes the refereed proceedings of the 11th international Work-Conference on Artificial Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers were carefully reviewed and selected from 202 submissions for presentation in two volumes. The second volume includes 76 papers organized in topical sections on video and image processing; hybrid artificial neural networks: models, algorithms and data; advances in machine learning for bioinformatics and computational biomedicine; biometric systems for human-machine interaction; data mining in biomedicine; bio-inspired combinatorial optimization; applying evolutionary computation and nature-inspired algorithms to formal methods; recent advances on fuzzy logic and soft computing applications; new advances in theory and applications of ICA-based algorithms; biological and bio-inspired dynamical systems; and interactive and cognitive environments. The last section contains 9 papers from the international workshop on Intelligent Systems for Context-Based Information Fusion, ISCIF 2011, held at IWANN 2011.

关键词： Computational Biology/Bioinformatics pattern recognition Artificial Intelligence data mining and Knowledge Discovery Models and Principles Bioinformatics

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