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Learning Brain Computer Interface model for Amyotrophic Lateral Sclerosis (ALS) people using Random Forest - Linear Discriminant Analysis (RF-LDA) algorithm 19

Learning Brain Computer Interface model for Amyotrophic Late...

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19th IEEE-India-Council International Conference (INDICON)

作者： Kumar, Arun S. Kannur, Anil Akash, C. K. Koushik, Venkatanath P. S. Kushal, S. Anwar, C. Shaik Nagarjuna Coll Engn & Technol Informat Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781665473507

As modern technology grows and the study of the human brain makes it easier to understand its complex activity, we are moving toward the acknowledgment of imagination. It is not a matter of comfort for various paralytics. It's the potential to tweak devices with a single thought. The improvement of the Brain Computer Interface may be the genuinely unambiguous movement of the last few years. Brain Computer Interface is a prompt correspondence channel between the mind and an electrical device. It is the new advancement in HCI. There are numerous things to investigate like Asynchronous method, small signal to noise ratio (SNR) and different noise sources are amongst the greatest challenges in EEG-based BCI application. Challenges are inherent in translating any new technology to practical and useful clinical applications and BCIs are no exception. We discuss the potential uses and users of BCI systems and address some of the limitations and challenges facing the field. In this paper we propose a new algorithm which combines LDA with Random forest algorithm which sub-divides the dataset and apply LDA algorithm to each subdivision and vote for the final output. Experimental results shows that our algorithm enhance the accuracy in printing the target letter.

关键词： Brain Computer Interface Signal Processing Feature Extraction classification algorithm

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Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective 29

Where to Handle an Exception? Recommending Exception Handlin...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Jia, Xiangyang Chen, Songqiang Zhou, Xingqi Li, Xintong Yu, Run Chen, Xu Xuan, Jifeng Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China NYU Tandon Sch Engn New York NY 10003 USA

ISBN: (纸本)9781665414036

Exception handling is an effective mechanism to guarantee software reliability in modern programming languages. An exception interrupts the program execution and propagates backwards along the call chain until the exception is caught by an exception handler. In software development practices, developers may be confused in determining where to place the exception handler in the call chain. The reason is that exception handling requires a developer to take a comprehensive consideration from a global perspective of the software project. In this paper, we propose an automatic approach EHAdvisor, which recommends exception handling locations from the global perspective of the project. EHAdvisor first trains a binary classification model based on four types of features, including architectural features, project features, functional features, and exception features. Then, for a new code snippet with exceptions, EHAdvisor predicts the exception catching probability for each method in the call chain based on the classification model and recommends Top-K exception handling locations based on the probability ranking. We conducted experiments on a dataset from 29 high-quality open source projects. Experimental results show that EHAdvisor achieves an average Top-1 recommendation success rate of 70.83% for across-project location recommendation and an average Top-1 accuracy of 86.21% for intra-project recommendation. Experiments on the importance scores show that global features, such as project features and architectural features, are evidently important to the recommendation of exception handling locations.

关键词： exception handling location recommendation machine learning global features classification algorithm

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Development of a deep learning system for preventive detection of heart failure due to pulmonary disease 11

Development of a deep learning system for preventive detecti...

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11th International Conference on Software Process Improvement (CIMPS) - Applications in Software Engineering

作者： Felix Jimenez, Axel Frederick Ibarra Belmonte, Isaul Sanchez Lee, Vania Stephany Jaramillo Avila, Uziel Parra Gonzalez, Ezra Federico Natl Polytech Inst Comp Syst Engn Zacatecas Zacatecas Mexico Ctr Res Math Zacatecas Zacatecas Mexico Ctr Res Math Dept Comp Sci Zacatecas Zacatecas Mexico

ISBN: (纸本)9798350398960

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.

关键词： heart failure pulmonary conditions condition prediction preventive diagnosis classification algorithm

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Classifying multiclass relationships between ASes using graph convolutional network

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Frontiers of Engineering Management 2022年第4期9卷 653-667页

作者： Songtao PENG Xincheng SHU Zhongyuan RUAN Zegang HUANG Qi XUAN Institute of Cyberspace Security College of Information EngineeringZhejiang University of TechnologyHangzhou 310023China Department of Electrical Engineering City University of Hong KongHong KongChina

Precisely understanding the business relationships between autonomous systems(ASes)is essential for studying the Internet *** date,many inference algorithms,which mainly focus on peer-to-peer(P2P)and provider-to-customer(P2C)binary classification,have been proposed to classify the AS relationships and have achieved excellent ***,business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent *** algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C *** this study,we focus on multiclassification of AS relationship for the first *** first summarize the differences between AS relationships under the structural and attribute features,and the reasons why multiclass relationships are difficult to be *** then introduce new features and propose a graph convolutional network(GCN)framework,AS-GCN,to solve this multiclassification problem under complex *** proposed framework considers the global network structure and local link features *** on real Internet topological data validate the effectiveness of our method,that is,*** proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task,with an overall metrics above 95%.

关键词： autonomous system multiclass relationship graph convolutional network classification algorithm Internet topology

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Trustworthiness of Spam Email Addresses using Machine Learning 21

Trustworthiness of Spam Email Addresses using Machine Learni...

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21st ACM Symposium on Document Engineering (DocEng)

作者： Janez-Martino, Francisco Alaiz-Rodriguez, Rocio Gonzalez-Castro, Victor Fidalgo, Eduardo Univ Leon Dept Elect Syst & Automat INCIBE Spanish Natl Cybersecur Inst Leon Spain

ISBN: (纸本)9781450385961

Cybercriminals have increasingly used spam email to send scams, phishing, malware and other frauds to organisations and people. They design sophisticated and contextualised emails to make them look trustworthy for users, being the sender addresses an essential part. Although cybersecurity agencies and companies develop products and organise courses for people to detect emails patterns, spam attacks are not totally avoided yet. This work presents a proof-of-concept methodology to give the user more meaningful information about trustworthiness to detect these harmful emails. For the first time in the literature, we present an email address dataset manually labelled into two classes, low and high quality. Moreover, we extracted 18 handcrafted features based on social engineering techniques and natural language properties. We evaluated four popular machine learning classifiers and obtained the best performance with Naive Bayes, i.e., 88.17% of accuracy and 0.808 of F1-Score. Additionally, we applied the InterpretML framework to find out the most relevant properties to eventually implement an automatic system able to inform about the trustworthiness of email addresses.

关键词： Spam Email Detection Machine Learning classification algorithm Cybersecurity Feature Extraction

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Gingivitis detection by Fractional Fourier Entropy and Biogeography-based Optimization 13

Gingivitis detection by Fractional Fourier Entropy and Bioge...

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13th IEEE/ACM International Conference on Utility and Cloud Computing (UCC)

作者： Yan, Yan Univ Leicester Sch Informat Univ Rd Leicester LE1 7RH Leics England Liming Vocat Univ Educ Informat Ctr Quanzhou 362000 Fujian Peoples R China

ISBN: (数字)9780738123943

ISBN: (纸本)9780738123943

As people keep a watch eye on the oral health, more people choose to go to professional dental hospitals for the regular dental examinations and diagnosis. It is well known that the dental diagnosis and treatment require excellent nursing skills and extensive experience by the dentists. Nervously, the number of experts is limited. However, the rapid increase in the number of diagnoses and the small number of professional dentists resulted in an increase in the daily diagnostic frequency of dentists, and the overworked working hours seriously affected the energy and diagnostic efficiency of dentists. This study for the sake of reduce the burden of dental diagnosis, proposes a computer-aided diagnosis method. This method classifies gingivitis images by using the image feature extraction method of fractional Fourier entropy (FRFE) and biogeography-based optimization (BBO) algorithm. The FRFE coefficient extracted from the image was used as the input feature vector, and the classification was carried out by the BBO algorithm with the optimal scheme of automatic screening. After 10-fold cross-validation, more effective healthy and pathological gingival image classification results were obtained compared with the latest methods.

关键词： fractional Fourier entropy detection of gingivitis biogeography-based optimization Multi-Layer Perception classification algorithm

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Multivariate statistical control of industrial process using neural networks

Multivariate statistical control of industrial process using...

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ISA Monterrey 2002 Conference

作者： Hernán, A Justo, M Karen, H Aaron, R UNEFA Dept Ingn Elect Maracay Estado Aragua Venezuela

ISBN: (纸本)1556177712

Traditional statistical process control is used in many industries to monitor process variables and is usually based on univariate data analysis. If one of the variables falls out of the acceptable limits, the process is out of control. However, many chemical processes are multivariate continuous processes with many highly correlated variables;therefore a univariate data analysis is not very effective for these types of processes. Statistical multivariable process control overcomes some of the limitations of singlevariable monitoring by simultaneously analyzing all the variables using principal component analysis (PCA). An unsupervised neural network using Hebbian learning rules is proposed. The proposed neural tools, Hotelling's (T-2) and Predicted Square Error (Q), are used to monitor the process. These statistical tools are applied to the outputs of the PCA model and are used to define a reference system based on the interpretation of logged data obtained from the process.

关键词： principal component analysis (PCA) Hotelling's T-2 statistic Predicted Square Error Q Hebbian learning rule classification algorithm

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Design and Implementation of a Multi-layered Employee Profile System for Power Enterprises Based on LSTM and Mini-batch K-means 5

Design and Implementation of a Multi-layered Employee Profil...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Wang, Ziheng Li, Xiaozhen Meng, Yu Zhang, Qian State Grid Information and Communication Industry Group Co. LTD. Beijing China

ISBN: (纸本)9798350309034

Abstract-To facilitate more efficient and convenient online office work, major enterprises have launched mobile portal applications, integrating various office micro-applications to provide employees with a unified entry point. However, employees in different positions have varying application demands and there is a lack of a personalized recommendation mechanism. Accurately profiling employees has thus become a research focus for many scholars. In response to this issue, this paper proposes a multi-layered employee profile system for power enterprises. We designed a data collection model suitable for processing massive logs, and used LSTM and mini-batch K-means algorithms to cluster employees at two levels: application usage and provincial distribution. Employee labels were established through statistical characteristics and professional analysis, and a classification model was trained for employee profiling. To verify the effectiveness of the system proposed in this paper, real data was collected to conduct experiments. The results were compared with traditional clustering algorithms and other outstanding profile algorithms from previous studies. The findings demonstrate that the profile system proposed in this paper achieves superior performance in profiling employees of power enterprises compared to other algorithms. © 2023 IEEE.

关键词： classification algorithm Clustering algorithm LSTM Mini-batch K-means User Profiling

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Study on Vegetation classification Based on Spectral Knowledge Base

Study on Vegetation Classification Based on Spectral Knowled...

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The International Conference on Intelligent Agriculture 2017(ICIA2017)

作者： Peng Liu Jingcheng Zhang Bin Wang Xuexue Zhang Kaihua Wu College of Life Information Science and Instrument Engineering Hangzhou Dianzi University

ISBN: (纸本)9783030061784

A framework about spectral based vegetation classification was proposed, which serves as a core methodology of the vegetation spectral knowledge base. The hyperspectral reflectances of 13 types of plants were measured by an ASD FieldSpec 4 spectroradiometer. Two forms of spectral features were used for representing the key spectral characteristics of plants,including Vegetation index(VI) and spectral shape features. Based on these spectral features, a sensitivity analysis was performed to identify the most important features for establishing the classifier. The analysis of variance(ANOVA) and the cross-correlation analysis were applied to derive the sensitivity of features and remove features that have high correlations. Then, a classification method for differentiating plants was established by coupling some spectral similarity measures(e.g., ED) with some classification methods(e.g.,BPANN and SVM). The results of discrimination analysis showed that a highest accuracy was produced by SVM with the OAA over 99% when using 7 sensitive VIs. The results suggested the framework about spectral based vegetation classification can form a basis for spectral knowledge base and application technology and further achieve a wide range of plant classification based on remote sensing.

关键词： Vegetation classification Hyperspectral Feature extraction classification algorithm

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Applied Biological Data Mining Based on Improved K-means Clustering algorithm And KNN Classifier in Protein Sub-cellular Localization 7

Applied Biological Data Mining Based on Improved K-means Clu...

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7th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Lei, Zhenfeng Wang, Shunfang Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Peoples R China

ISBN: (纸本)9781467399043

With the completion of the human genome sequencing, a large number of data especially amino acid sequences floods into biological database, How to analyze these data quickly and even predict the structure and function of protein correctly have become hot topics in recent years, In this paper, we mainly study K-means clustering algorithm and KNN classifier in amino acid sequences of complicated data, which are applied in the prediction of protein sub-cellular localization. In many cases, fuzzy boundary and unbalance are frequently appeared among biological data. The accuracy will be lower, if we make a prediction through traditional KNN and K-means clustering algorithm directly. Firstly, in order to make clear the unbalance, we propose the within-class thought to make sure that training samples in each class around the testing sample are selected and we introduce membership to tell which class the testing sample belongs to. Then, we bring in rough sets and membership to solve the fuzzy boundary. Particularly, we apply correlation coefficient in the rough sets to better reflect the relationship among data objects. The experimental results based on protein sub-cellular localization prediction show that the methods proposed newly better work than the traditional methods.

关键词： data mining clustering algorithm classification algorithm protein sub-cellular localization

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