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Scalable Ensemble learning by Adaptive Sampling

Scalable Ensemble Learning by Adaptive Sampling

引用

international conference on Machine learning and Applications (ICMLA)

作者： Jianhua Chen Division of Computer Science and Engineering Louisiana State University Baton Rouge LA

Scalability has become an increasingly critical problem for successful data mining and knowledge discovery applications in real world where we often encounter extremely huge data sets that will render the traditional learning algorithms infeasible. Among various approaches to scalable learning, sampling techniques can be exploited to address the issue of scalability. this paper presents a brief outline on how to utilize the new sampling method in [3] to develop a scalable ensemble learning method with Boosting. Preliminary experimental results using benchmark data sets from the UC-Irvine ML data repository are also presented confirming the efficiency and competitive prediction accuracy of the proposed adaptive boosting method.

关键词： Accuracy Boosting Sampling methods Computer science data mining

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Hate Speech Detection in Social Media Using BiLSTM 11

Hate Speech Detection in Social Media Using BiLSTM

引用

11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Kuruvilla, Sheen Maria Fathima, Shifa Suryalekshmi, S. Saju, Varsha Monica Pattani, Rose V Saintgits College of Engineering Computer Science & Engineering Kottayam India

ISBN: (纸本)9798350379136

Social media platforms also function as breeding grounds for hate speech. Hate speech is offensive language targeting people based on their characteristics, including gender, race, or religion. the existing hate speech detection system relies on sentiment analysis, which is limited to processing static data that has been extracted from social media platforms beforehand. this research focuses on the development of a hate speech detection system specifically designed for social media platforms. By utilizing Bidirectional Long Short-Term Memory (BiLSTM) networks, the proposed system seeks to precisely detect and categorize hate speech in real time, allowing for rapid mitigation and intervention measures. the network can grasp both past and future context by processing text inputs bidirectionally, enabling a thorough comprehension of semantic linkages and linguistic nuances. the system integrates a chat evaluator module to monitor real-time conversations, issuing warnings and blocking users based on a threshold of detected hate speech occurrences. the implementation of a bidirectional strategy improves the model's overall accuracy and decreases false positives by enhancing its capacity to identify minute fluctuations in hate speech patterns. this initiative contributes to the continuous endeavors aimed at combating hate speech, thereby fostering a digital environment that is more inclusive and secure. © 2024 IEEE.

关键词： Adversarial machine learning

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Modelling and clustering of gene expressions using RBFs and a shape similarity metric

引用

5th international conference on intelligent data engineering and automated learning, IDEAL 2004

作者： Möller-Levet, Carla S. Yin, Hujun Electrical Engineering and Electronics University of Manchester Institute of Science and Technology ManchesterM60 IQD United Kingdom

ISBN: (纸本)3540228810

this paper introduces a novel approach for gene expression time-series modelling and clustering using neural networks and a shape similarity metric. the modelling of gene expressions by the Radial Basis Function (RBF) neural networks is proposed to produce a more general and smooth characterisation of the series. Furthermore, we identified that the use of the correlation coefficient of the derivative of the modelled profiles allows the comparison of profiles based on their shapes and the distributions of time points. the series are grouped into similarly shaped profiles using a correlation based fuzzy clustering algorithm. A well known dataset is used to demonstrate the proposed approach and a set of known genes are used as a benchmark to evaluate its performance. the results show the biological relevance and indicate that the proposed method is a useful technique for gene expression time-series analysis. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Gene expression

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Robot Obstacle Avoidance and Navigation Control Algorithm Research Based on Multi-Sensor Information Fusion

Robot Obstacle Avoidance and Navigation Control Algorithm Re...

引用

international conference on intelligent Computation Technology and Automation, ICICTA

作者： Ziyang Wang The School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

Multi-sensor information fusion technology has very extensive application prospect in robot obstacle avoidance. To solve the problems of robot obstacle avoidance and path scheduling for mobile robots, this article proposes a T-S based FNN multi-sensor information fusion approach integrated with fuzzy logic and neural network. It analyzes the learning algorithm based on gradient in FNN and improved the computing process, which avoids possible oscillation and reduces training time of network. then the secondary fusion scheme implemented on robot obstacle avoidance and navigation control is designed. the simulations verify that our approach is feasible and effective in obstacle avoidance and path planning for mobile robots.

关键词： Handheld computers Automation

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Power Terminal data Security and Efficient Management Mechanism Based on Master-Slave Blockchain 11th

Power Terminal Data Security and Efficient Management Mechan...

引用

11th international conference on Computer engineering and Networks, CENet2021

作者： Wang, Shaoying Yang, Huifeng Gao, Lifang Li, Qimeng Lv, Pengpeng Lu, Xin Lin, Peng ICT Branch State Grid Hebei Electric Power Co. Ltd. Hebei Shi Jiazhuang050000 China Beijing VectInfo Technologies Co. Ltd. Beijing100088 China

ISBN: (纸本)9789811665530

How to realize the safe and efficient use of power terminal equipment data is a key research content to enhance the value of smart grid data. In order to solve this problem, based on the master-slave blockchain theory, this paper designs an intelligent terminal data management system architecture based on the master-slave blockchain. the architecture includes three modules: master blockchain, slave blockchain, and system service interface. In order to improve the security of data in the process of data use, this paper conducts research from the two dimensions of data security storage and data access control, and proposes a data security storage mechanism and a data access control mechanism. In order to improve the efficiency of data query when the amount of data is large, this paper adopts the grouping bloom filtering strategy and the B+ tree index strategy to reduce the number of data queries and improve the efficiency of data query. In the experimental part, from the performance analysis of the improved Bloom filter based on grouping and the performance analysis of the block query mechanism based on the B+ tree index, it is verified that the data usage mechanism proposed in this paper has high block creation and data query efficiency. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forestry

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the use of telematics to monitor traffic in urban areas: theory and Applications

The use of telematics to monitor traffic in urban areas: The...

引用

international conference on intelligent Transportation

作者： Nikolas Geroliminis Masao Kuwahara Department of Civil and Engineering University of Minnesota Minneapolis MN USA Institute of Industrial Sciences University of Tokyo Japan

To alleviate urban congestion, macroscopic feedback control strategies have recently introduced which rely on real-time observation of relevant spatially aggregated measures of traffic performance. Ample monitoring of the system is necessary for those strategies to be successfully implemented. An efficient and practical methodology is developed to estimate the fraction of vehicles in a city that should be tracked. A filter to identify passenger-carrying taxis based on GPS data is described. An application in a real city and comparison with detector data show that the average speed can be predicted quite accurately.

关键词： Telematics Monitoring Urban areas Global Positioning System intelligent transportation systems Cities and towns Filters Detectors Road vehicles Vehicle driving

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A Machine learning Approach for Multiclass Sentiment Analysis of Twitter data: A Review 5th

A Machine Learning Approach for Multiclass Sentiment Analysi...

引用

5th international conference on Emerging Technologies in Computer engineering: Cognitive Computing and intelligent IoT, ICETCE 2022

作者： Chougule, Bhagyashree B. Patil, Ajit S. Shivaji University Kolhapur416234 India

ISBN: (纸本)9783031070112

Sentiment analysis or opinion mining is a prominent and most demanding research topic in today’s world. the main idea behind this research topic is to recognize the user’s opinions and emotions towards the aspect of service or product via a text basis. Sentiment analysis involves mining text, lexicon construction, extracting features and finally finding polarity of text. Even though numerous amounts of research work were conducted in this field through different methods, opinion mining is still considered a challenging field for research. Most of the prior research concentrated on the binary or ternary classification of sentiments such as positive, negative, neutral. Some studies have done an analysis of Twitter sentiment based on ordinal regression, but by turning the problem of ordinal regression into a problem of binary classification. the aim of this study is to review the multiclass sentiment analysis of Twitter text data using an automated i.e., machine learning approach. this review paper intends to focus on existing work for Twitter sentiment analysis with multiple polarity categorization and explore gaps with future scope in the said research area. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Social networking (online)

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Promsearch: A hybrid approach to human core-promoter prediction

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5th international conference on intelligent data engineering and automated learning, IDEAL 2004

作者： Kim, Byoung-Hee Park, Seong-Bae Zhang, Byoung-Tak Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Seoul151-744 Korea Republic of

ISBN: (纸本)3540228810

this paper presents an effective core-promoter prediction system on human DNA sequence. the system, named PromSearch, employs a hybrid approach which combines search-by-content method and search-by-signal method. Global statistics of promoter-specific contents are included to represent new significant information underlying the proximal and downstream region around transcription start site (TSS) of DNA sequence. Local signal features such as TATA box and CAAT box are encoded by the position weight matrix (PWM) method. In the experiment for the sequence set from the review by ***, PromSeach shows 47% positive predictive value which surpasses most of previously systems. On large genomic sequences, it shows reduced false positive rate while preserving true positive rate. © Springer-Verlag Berlin Heidelberg 2004.

关键词： DNA sequences

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6th international Workshop on Security of Industrial Control Systems and Cyber-Physical Systems, CyberICPS 2020, 2nd international Workshop on Security and Privacy Requirements engineering, SECPRE 2020, and 3rd international Workshop on Attacks and Defenses for Internet-of-things, ADIoT 2020, held in conjunction with 25th European Symposium on Research in Computer Security, ESORICS 2020

6th International Workshop on Security of Industrial Control...

引用

6th international Workshop on Security of Industrial Control Systems and Cyber-Physical Systems, CyberICPS 2020, 2nd international Workshop on Security and Privacy Requirements engineering, SECPRE 2020, and 3rd international Workshop on Attacks and Defenses for Internet-of-things, ADIoT 2020, held in conjunction with 25th European Symposium on Research in Computer Security, ESORICS 2020

ISBN: (纸本)9783030643294

the proceedings contain 16 papers. the special focus in this conference is on Security of Industrial Control Systems and Cyber-Physical Systems. the topics include: Integrated Analysis of Safety and Security Hazards in Automotive Systems;GDPR Compliance: Proposed Guidelines for Cloud-Based Health Organizations;aligning the Concepts of Risk, Security and Privacy Towards the Design of Secure intelligent Transport Systems;identifying Implicit Vulnerabilities through Personas as Goal Models;Cooperative Speed Estimation of an RF Jammer in Wireless Vehicular Networks;extended Abstract: Towards Physical-Layer Authentication for Backscatter Devices;p2Onto: Making Privacy Policies Transparent;extended Abstract - Transformers: Intrusion Detection data in Disguise;attack Path Analysis for Cyber Physical Systems;identifying and Analyzing Implicit Interactions in a Wastewater Dechlorination System;a Survey of Cryptography-Based Authentication for Smart Grid Communication;cybersecurity Awareness Platform with Virtual Coach and automated Challenge Assessment;ioT Vulnerability Scanning: A State of the Art;learning from Vulnerabilities - Categorising, Understanding and Detecting Weaknesses in Industrial Control Systems;self Adaptive Privacy in Cloud Computing Environments: Identifying the Major Socio-Technical Concepts;definition and Verification of Security Configurations of Cyber-Physical Systems.

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Predicting length of stay using regression and Machine learning models in Intensive Care Unit: A pilot study 21

Predicting length of stay using regression and Machine Learn...

引用

11th international conference on Biomedical engineering and Technology, ICBET 2021

作者： Picone, Ilaria Latessa, Imma Fiorillo, Antonella Scala, Arianna Angela Trunfio, Teresa Triassi, Maria Department of Advanced Biomedical Sciences University of Naples Federico II Naples Italy Department of Public Health University of Naples Federico II Naples Italy Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy Department of Public Health Univ. of Naples Federico II and Interdepartmental Ctr. for Res. in Hlthcare. Mgmt. and Innov. in Hlthcare.

ISBN: (纸本)9781450387897

Healthcare Associated Infection (HAI) is a major health problem in several departments of the hospital sector. these infections cause prolonged length of stay (LOS), complications, and increased hospital costs. In this paper, medical record data of 415 patients admitted to in the Adult and Neonatal Intensive Care Unit (ICU) where there was a high risk of contracting HAIs, were used collectively. the aim was to create models capable of predicting LOS, measured in days, considering preoperative clinical information. Multiple linear regression analysis and Machine learning (ML) regression analysis were performed. Subsequently, the LOS was grouped by weeks and classified with the ML classification algorithms. Multiple linear regression was implemented with IBM SPSS, the coefficient of determination (R2) was equal to 0.343. A regression with ML algorithms is performed with the Knime analysis platform. the best R2 was obtained from the Random Forest (R2 =0.414) and Gradient Boosted Tree (R2 =0.382) algorithms. Regarding the classification analysis, the RF and Multi-Layer Perceptron algorithms showed accuracy respectively 49.398% and 46.988%, an error of 50.602% and 53.012%. the goal was to create a model to support physicians in evaluating the hospitalization of patients at risk of HAI in the ICU. (H. De Koning, 2006) © 2021 ACM.

关键词： Intensive care units

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