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Journal articles on the topic 'Machine learning for information improvement'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Zhang, Zhi Feng, Cheng Gan, Xiao Jian Ding, and Zeng Yu Cai. "Research on Optimization Method of Extreme Learning Machine with Application of Information Technology." Advanced Materials Research 859 (December 2013): 23–27. http://dx.doi.org/10.4028/www.scientific.net/amr.859.23.

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Based on the research of extreme learning machine and support vector machine, this paper does the research on the optimization method on extreme learning machine. This paper suggests an optimization model of extreme learning machine based on the improvement of the old model, and this model has obvious improvement on generalization ability and learning parameter ability. This approach can improve the development efficiency in the information technology, the experiment indicate this approach is efficient.

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.

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Sumner, Joel, and Adel Alaeddini. "Analysis of Feature Extraction Methods for Prediction of 30-Day Hospital Readmissions." Methods of Information in Medicine 58, no. 06 (2019): 213–21. http://dx.doi.org/10.1055/s-0040-1702159.

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Abstract Objectives This article aims to determine possible improvements made by feature extraction methods to the machine learning prediction methods for predicting 30-day hospital readmissions. Methods The study evaluates five feature extraction methods including principal component analysis (PCA), kernel principal component analysis (KPCA), isomap, Laplacian eigenmaps, and locality preserving projections (LPPs) for improving the accuracy of nine machine learning prediction methods in predicting 30-day hospital readmissions. The specific prediction methods considered include logistic regress

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MAHAJAN, SHWETA. "News Classification Using Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (2021): 23–27. http://dx.doi.org/10.17762/ijritcc.v9i5.5464.

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There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorit

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Rahmati, Marzie, and Mohammad Ali Zare Chahooki. "Improvement in bug localization based on kernel extreme learning machine." Journal of Communications Technology, Electronics and Computer Science 5 (April 30, 2016): 1. http://dx.doi.org/10.22385/jctecs.v5i0.77.

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Bug localization uses bug reports received from users, developers and testers to locate buggy files. Since finding a buggy file among thousands of files is time consuming and tedious for developers, various methods based on information retrieval is suggested to automate this process. In addition to information retrieval methods for error localization, machine learning methods are used too. Machine learning-based approach, improves methods of describing bug report and program code by representing them in feature vectors. Learning hypothesis on Extreme Learning Machine (ELM) has been recently ef

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Padovani de Souza, Kleber, João Carlos Setubal, André Carlos Ponce de Leon F. de Carvalho, Guilherme Oliveira, Annie Chateau, and Ronnie Alves. "Machine learning meets genome assembly." Briefings in Bioinformatics 20, no. 6 (2018): 2116–29. http://dx.doi.org/10.1093/bib/bby072.

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Abstract Motivation: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable ex

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Manu, Y. M., and G. K. Ravikumar. "Survey on Machine Learning Based Video Analytics Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 11 (2020): 4989–95. http://dx.doi.org/10.1166/jctn.2020.9000.

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Video information has turned into the biggest wellspring of information expended all inclusive. Because of the fast development of applications which are related to video applications and requests of boosting for greater surpassing video administrations, video information volume has expanding violently around the world, which is the serious challenge for media processing, capacity and transmission. Video coding by packing recordings into a lot littler size is also key arrangements; in any case, its advancement has turned out to be soaked somewhat while the pressure proportion consistently deve

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Ji, Meng, Yanmeng Liu, and Tianyong Hao. "Predicting Health Material Accessibility: Development of Machine Learning Algorithms." JMIR Medical Informatics 9, no. 9 (2021): e29175. http://dx.doi.org/10.2196/29175.

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Background Current health information understandability research uses medical readability formulas to assess the cognitive difficulty of health education resources. This is based on an implicit assumption that medical domain knowledge represented by uncommon words or jargon form the sole barriers to health information access among the public. Our study challenged this by showing that, for readers from non-English speaking backgrounds with higher education attainment, semantic features of English health texts that underpin the knowledge structure of English health texts, rather than medical jar

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Karamitsos, Ioannis, Saeed Albarhami, and Charalampos Apostolopoulos. "Applying DevOps Practices of Continuous Automation for Machine Learning." Information 11, no. 7 (2020): 363. http://dx.doi.org/10.3390/info11070363.

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This paper proposes DevOps practices for machine learning application, integrating both the development and operation environment seamlessly. The machine learning processes of development and deployment during the experimentation phase may seem easy. However, if not carefully designed, deploying and using such models may lead to a complex, time-consuming approaches which may require significant and costly efforts for maintenance, improvement, and monitoring. This paper presents how to apply continuous integration (CI) and continuous delivery (CD) principles, practices, and tools so as to minim

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Bagui, Sikha, and Daniel Benson. "Android Adware Detection Using Machine Learning." International Journal of Cyber Research and Education 3, no. 2 (2021): 1–19. http://dx.doi.org/10.4018/ijcre.2021070101.

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Adware, an advertising-supported software, becomes a type of malware when it automatically delivers unwanted advertisements to an infected device, steals user information, and opens other vulnerabilities that allow other malware and adware to be installed. With the rise of more and complex evasive malware, specifically adware, better methods of detecting adware are required. Though a lot of work has been done on malware detection in general, very little focus has been put on the adware family. The novelty of this paper lies in analyzing the individual adware families. To date, no work has been

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Kozik, Rafał, Marek Pawlicki, and Michał Choraś. "Cost-Sensitive Distributed Machine Learning for NetFlow-Based Botnet Activity Detection." Security and Communication Networks 2018 (December 20, 2018): 1–8. http://dx.doi.org/10.1155/2018/8753870.

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The recent advancements of malevolent techniques have caused a situation where the traditional signature-based approach to cyberattack detection is rendered ineffective. Currently, new, improved, potent solutions incorporating Big Data technologies, effective distributed machine learning, and algorithms countering data imbalance problem are needed. Therefore, the major contribution of this paper is the proposal of the cost-sensitive distributed machine learning approach for cybersecurity. In particular, we proposed to use and implemented cost-sensitive distributed machine learning by means of

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Vazirov, Etibar. "MACHINE LEARNING-BASED MODELING FOR PERFORMANCE IMPROVEMENT IN AN EXASCALE SYSTEMS." Azerbaijan Journal of High Performance Computing 3, no. 2 (2020): 223–33. http://dx.doi.org/10.32010/26166127.2020.3.2.223.233.

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The combination of heterogeneous resources within exascale architectures guarantees to be capable of revolutionary compute for scientific applications. There will be some data about the status of the current progress of jobs, hardware and software, memory, and network resource usage. This provisional information has an irreplaceable value in learning to predict where applications may face dynamic and interactive behavior when resource failures occur. What is proposed in this paper is building a scalable framework that uses special performance information collected from all other sources. It wi

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MacFarlane, Andrew, Sondess Missaoui, and Sylwia Frankowska-Takhari. "On Machine Learning and Knowledge Organization in Multimedia Information Retrieval." KNOWLEDGE ORGANIZATION 47, no. 1 (2020): 45–55. http://dx.doi.org/10.5771/0943-7444-2020-1-45.

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Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field,

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Munteanu, Cristian R., Marcos Gestal, Yunuen G. Martínez-Acevedo, Nieves Pedreira, Alejandro Pazos, and Julián Dorado. "Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning." International Journal of Molecular Sciences 20, no. 18 (2019): 4362. http://dx.doi.org/10.3390/ijms20184362.

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In this work, we improved a previous model used for the prediction of proteomes as new B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its sequence, a known reference epitope sequence under specific experimental conditions. The peptide sequences were transformed into molecular descriptors of sequence recurrence networks and were mixed under experimental conditions. The new models were generated using 709,100 instances of pair descriptors for query and reference peptide sequences. Using perturbations of the initial descriptors under sequence or

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Hu, Ya-Han, Chun-Tien Tai, Chih-Fong Tsai, and Min-Wei Huang. "Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach." Journal of Healthcare Engineering 2018 (August 19, 2018): 1–9. http://dx.doi.org/10.1155/2018/3948245.

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Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage. A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study. Ten independent variables, including demographic information, laboratory data

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Na, Gyoung S., Hyun Woo Kim, and Hyunju Chang. "Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis." Journal of Chemical Information and Modeling 60, no. 3 (2020): 1137–45. http://dx.doi.org/10.1021/acs.jcim.9b00816.

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Garanayak, Mamata, Goutam Sahu, Sachi Nandan Mohanty, and Alok Kumar Jagadev. "Agricultural Recommendation System for Crops Using Different Machine Learning Regression Methods." International Journal of Agricultural and Environmental Information Systems 12, no. 1 (2021): 1–20. http://dx.doi.org/10.4018/ijaeis.20210101.oa1.

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Agriculture is a foremost field within the world, and it's the backbone in the Republic of India. Agriculture has been in poor condition. The impact of temperature variations and its uncertainty has engendered the bulk of the agricultural crops to be overripe in terms of their manufacturing. A correct forecast of crop expansion is a vital character in crop forecast management. Such forecasts will hold up the federated industries for accomplishing the provision of their occupation. ML is the method of finding new models from giant information sets. Numerous regressive ways like random forest, l

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Ņikiforova, Oksana, Vitaly Zabiniako, Jurijs Kornienko, Madara Gasparoviča-Asīte, and Amanda Siliņa. "Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems." Applied Computer Systems 26, no. 1 (2021): 22–30. http://dx.doi.org/10.2478/acss-2021-0003.

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Abstract Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper

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Thepade, Sudeep D., and Gaurav Ramnani. "Haar Wavelet Pyramid-Based Melanoma Skin Cancer Identification With Ensemble of Machine Learning Algorithms." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (2021): 1–15. http://dx.doi.org/10.4018/ijhisi.20211001.oa24.

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Melanoma is a mortal type of skin cancer. Early detection of melanoma significantly improves the patient’s chances of survival. Detection of melanoma at an early juncture demands expert doctors. The scarcity of such expert doctors is a major issue with healthcare systems globally. Computer-assisted diagnostics may prove helpful in this case. This paper proposes a health informatics system for melanoma identification using machine learning with dermoscopy skin images. In the proposed method, the features of dermoscopy skin images are extracted using the Haar wavelet pyramid various levels. Thes

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Winter, Jenifer Sunrise, and Elizabeth Davidson. "Governance of artificial intelligence and personal health information." Digital Policy, Regulation and Governance 21, no. 3 (2019): 280–90. http://dx.doi.org/10.1108/dprg-08-2018-0048.

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Purpose This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions. Design/methodology/approach This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance. Findings This paper argues that these characteristics of machine learning will overwhelm existing data governance

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Hasan, Omar Shakir, and Ibrahim Ahmed Saleh. "Development of heart attack prediction model based on ensemble learning." Eastern-European Journal of Enterprise Technologies 4, no. 2(112) (2021): 26–34. http://dx.doi.org/10.15587/1729-4061.2021.238528.

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With the advent of the data age, the continuous improvement and widespread application of medical information systems have led to an exponential growth of biomedical data, such as medical imaging, electronic medical records, biometric tags, and clinical records that have potential and essential research value. However, medical research based on statistical methods is limited by the class and size of the research community, so it cannot effectively perform data mining for large-scale medical information. At the same time, supervised machine learning techniques can effectively solve this problem

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HOSTE, V., I. HENDRICKX, W. DAELEMANS, and A. VAN DEN BOSCH. "Parameter optimization for machine-learning of word sense disambiguation." Natural Language Engineering 8, no. 4 (2002): 311–25. http://dx.doi.org/10.1017/s1351324902003005.

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Various Machine Learning (ML) approaches have been demonstrated to produce relatively successful Word Sense Disambiguation (WSD) systems. There are still unexplained differences among the performance measurements of different algorithms, hence it is warranted to deepen the investigation into which algorithm has the right ‘bias’ for this task. In this paper, we show that this is not easy to accomplish, due to intricate interactions between information sources, parameter settings, and properties of the training data. We investigate the impact of parameter optimization on generalization accuracy

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Tian, Huixin, Minwei Shuai, Kun Li, and Xiao Peng. "An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors." Complexity 2019 (May 2, 2019): 1–12. http://dx.doi.org/10.1155/2019/5353296.

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With the continuous improvement of automation in industrial production, industrial process data tends to arrive continuously in many cases. The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms. According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data. The ILES aggregates multiple sublearning machines by different weights for better accuracy.

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Kocaman, Sultan, and Nadire Ozdemir. "Improvement of Disability Rights via Geographic Information Science." Sustainability 12, no. 14 (2020): 5807. http://dx.doi.org/10.3390/su12145807.

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Rights, legal regulations, and practices often arise from societal and scientific developments, and societal transformations may originate from new legal regulations as well. Basic rights can be re-defined with advancements in science and technology. In such an evolutional loop, where mutual supply is obvious, combined legal and technological frameworks should be exercised and developed for practicing human rights. The main aim of this article is to propose a conceptual and methodological framework for the improvement of disability rights in the light of recent advancements in geographic infor

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Sidorov, Denis, Fang Liu, and Yonghui Sun. "Machine Learning for Energy Systems." Energies 13, no. 18 (2020): 4708. http://dx.doi.org/10.3390/en13184708.

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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking

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Abo-Tabik, Maryam, Yael Benn, and Nicholas Costen. "Are Machine Learning Methods the Future for Smoking Cessation Apps?" Sensors 21, no. 13 (2021): 4254. http://dx.doi.org/10.3390/s21134254.

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Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting o

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Geetha, Dr V., Dr C. K. Gomathy*, T. Harshitha, and P. Vijay Nagendra Varma. "A Traffic Prediction for Intelligent Transportation System using Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 166–68. http://dx.doi.org/10.35940/ijeat.d2426.0410421.

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Traffic control has been an issue for a long time from the past. The modern world demands Technology. Now a days cars are one of the main methods of improvement in technology. Intelligent Traffic System is also known as Intelligent Transportation System apply communication and information technology to find the solution for the Traffic control issues. Intelligent Transportation System represents the main problem in transportation. ITS is a program .it is used to improve the efficiency of transportation through advanced technologies by using sensors and communication. Some of the problems like

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Mihaylov, Iliyan, Maria Nisheva, and Dimitar Vassilev. "Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies." Information 10, no. 3 (2019): 93. http://dx.doi.org/10.3390/info10030093.

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The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and

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Liu, Aodi, Xuehui Du, and Na Wang. "Efficient Access Control Permission Decision Engine Based on Machine Learning." Security and Communication Networks 2021 (February 17, 2021): 1–11. http://dx.doi.org/10.1155/2021/3970485.

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Access control technology is critical to the safe and reliable operation of information systems. However, owing to the massive policy scale and number of access control entities in open distributed information systems, such as big data, the Internet of Things, and cloud computing, existing access control permission decision methods suffer from a performance bottleneck. Consequently, the large access control time overhead affects the normal operation of business services. To overcome the above-mentioned problem, this paper proposes an efficient permission decision engine scheme based on machine

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A P, Ajees, Manju K, and Sumam Mary Idicula. "An Improved Word Representation for Deep Learning Based NER in Indian Languages." Information 10, no. 6 (2019): 186. http://dx.doi.org/10.3390/info10060186.

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Named Entity Recognition (NER) is the process of identifying the elementary units in a text document and classifying them into predefined categories such as person, location, organization and so forth. NER plays an important role in many Natural Language Processing applications like information retrieval, question answering, machine translation and so forth. Resolving the ambiguities of lexical items involved in a text document is a challenging task. NER in Indian languages is always a complex task due to their morphological richness and agglutinative nature. Even though different solutions we

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J. Prasanna kumar and Dr. A.V.Krishna Prasad, D. Venkat Sai. "Reconstruction Process of Geomagnetic Data using Machine Learning." International Journal for Modern Trends in Science and Technology 6, no. 10 (2020): 113–17. http://dx.doi.org/10.46501/ijmtst061020.

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The geomagnetic data plays a important role in understanding the evolutionary process of Earth’s magnetic field, as it provides necessary information for near-surface exploration, unexploded explosive ordnance detection, and so on. To reconstruct the geomagnetic data, this project presents a geomagnetic data reconstruction method based on machine learning techniques. The traditional linear approaches are prone to time inefficiency and involves high labor cost, while the proposed approach has a significant improvement. In this project, three classic machine learning models, support vector machi

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Lu, Weizhong, Zhengwei Song, Yijie Ding, et al. "Use Chou’s 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information." BioMed Research International 2020 (July 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/6984045.

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The knowledge of DNA-binding proteins would help to understand the functions of proteins better in cellular biological processes. Research on the prediction of DNA-binding proteins can promote the research of drug proteins and computer acidified drugs. In recent years, methods based on machine learning are usually used to predict proteins. Although great predicted performance can be achieved via current methods, researchers still need to invest more research in terms of the improvement of predicted performance. In this study, the prediction of DNA-binding proteins is studied from the perspecti

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Cesarini, Luigi, Rui Figueiredo, Beatrice Monteleone, and Mario L. V. Martina. "The potential of machine learning for weather index insurance." Natural Hazards and Earth System Sciences 21, no. 8 (2021): 2379–405. http://dx.doi.org/10.5194/nhess-21-2379-2021.

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Abstract. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models wer

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Himilda, Rispani, and Ragil Andika Johan. "Klasifikasi Jenis Kendaraan Menggunakan Metode Extreme Learning Machine." JTIM : Jurnal Teknologi Informasi dan Multimedia 2, no. 4 (2021): 237–43. http://dx.doi.org/10.35746/jtim.v2i4.118.

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The number of vehicles in Indonesia has increased each year, both two-wheeled and four-wheeled vehicles; this is inversely proportional to the development of road infrastructure in Indonesia, which has not experienced much change or improvement. Supposedly, with the increase in the number of vehicles, road infrastructure must also keep pace so that things such as the accumulation of cars on the road do not occur, traffic accidents and congestion become obstacles to carrying out activities. Therefore, it is necessary to make a system to detect and classify vehicles' types in this study using tw

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Senders, Joeky Tamba, Maya Harary, Brittany Morgan Stopa, et al. "Information-Based Medicine in Glioma Patients: A Clinical Perspective." Computational and Mathematical Methods in Medicine 2018 (June 13, 2018): 1–6. http://dx.doi.org/10.1155/2018/8572058.

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Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical management to the individual patient. Randomized trials have laid the foundation for many neuro-oncological guidelines. Despite this, their findings focus on group-level estimates. Given our current tools, we are limited in our ability to guide patients on what therapy is best for them as individuals, or even how long they should expect to survive. Machine

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MONZ, CHRISTOF. "Machine learning for query formulation in question answering." Natural Language Engineering 17, no. 4 (2011): 425–54. http://dx.doi.org/10.1017/s1351324910000276.

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AbstractResearch on question answering dates back to the 1960s but has more recently been revisited as part of TREC's evaluation campaigns, where question answering is addressed as a subarea of information retrieval that focuses on specific answers to a user's information need. Whereas document retrieval systems aim to return the documents that are most relevant to a user's query, question answering systems aim to return actual answers to a users question. Despite this difference, question answering systems rely on information retrieval components to identify documents that contain an answer t

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Inyang, Udoinyang Godwin, Emem Etok Akpan, and Oluwole Charles Akinyokun. "A Hybrid Machine Learning Approach for Flood Risk Assessment and Classification." International Journal of Computational Intelligence and Applications 19, no. 02 (2020): 2050012. http://dx.doi.org/10.1142/s1469026820500121.

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Communities globally experience devastating effects, high monetary loss and loss of lives due to incidents of flood and other hazards. Inadequate information and awareness of flood hazard make the management of flood risks arduous and challenging. This paper proposes a hybridized analytic approach via unsupervised and supervised learning methodologies, for the discovery of pieces of knowledge, clustering and prediction of flood severity levels (FSL). A two-staged unsupervised learning based on [Formula: see text]-means and self-organizing maps (SOM) was performed on the unlabeled flood dataset

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HANDA, HISASHI, MITSURU BABA, TADASHI HORIUCHI, and OSAMU KATAI. "A NOVEL HYBRID FRAMEWORK OF COEVOLUTIONARY GA AND MACHINE LEARNING." International Journal of Computational Intelligence and Applications 02, no. 01 (2002): 33–52. http://dx.doi.org/10.1142/s1469026802000415.

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In this paper, we will propose a novel framework of hybridization of Coevolutionary Genetic Algorithm and Machine Learning. The Coevolutionary Genetic Algorithm (CGA) which has already been proposed by Handa et al. consists of two GA populations: the first GA (H-GA) population searches for the solutions in given problems, and the second GA (P-GA) population searches for effective schemata of the H-GA. The CGA adopts the notion of commensalism, a kind of co-evolution. The new hybrid framework incorporates a schema extraction mechanism by Machine Learning techniques into the CGA. Considerable im

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Chen, Songlin, Hong Wen, Jinsong Wu, et al. "Physical-Layer Channel Authentication for 5G via Machine Learning Algorithm." Wireless Communications and Mobile Computing 2018 (October 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/6039878.

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By utilizing the radio channel information to detect spoofing attacks, channel based physical layer (PHY-layer) enhanced authentication can be exploited in light-weight securing 5G wireless communications. One major obstacle in the application of the PHY-layer authentication is its detection rate. In this paper, a novel authentication method is developed to detect spoofing attacks without a special test threshold while a trained model is used to determine whether the user is legal or illegal. Unlike the threshold test PHY-layer authentication method, the proposed AdaBoost based PHY-layer authe

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Huang, Shigao, Jie Yang, Simon Fong, and Qi Zhao. "Mining Prognosis Index of Brain Metastases Using Artificial Intelligence." Cancers 11, no. 8 (2019): 1140. http://dx.doi.org/10.3390/cancers11081140.

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This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods were selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set with particle swarm optimization (MIRSPSO) methods to predict patient’s prognosis with the highest accuracy at area under the curve (AUC) = 0.978 ± 0.06. The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29%,

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Chen, Hong, Hongdong Zhao, Baoqiang Qi, Shi Wang, Nan Shen, and Yuxiang Li. "Human motion recognition based on limit learning machine." International Journal of Advanced Robotic Systems 17, no. 5 (2020): 172988142093307. http://dx.doi.org/10.1177/1729881420933077.

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With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action f

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Zhang, Yongshan, Xinwei Jiang, Xinxin Wang, and Zhihua Cai. "Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine." Remote Sensing 11, no. 17 (2019): 1983. http://dx.doi.org/10.3390/rs11171983.

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Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental information and spatial patterns provide the complementary information. With both spectral features and spatial patterns, hyperspectral image (HSI) applications can be fully explored and the classification performance can be greatly improved. In reality, spatial patterns can be extracted to represent a line, a clustering of points or image texture, which denote

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Lasisi, Ahmed, Pengyu Li, and Jian Chen. "Hybrid Machine Learning and Geographic Information Systems Approach — A Case for Grade Crossing Crash Data Analysis." Advances in Data Science and Adaptive Analysis 12, no. 01 (2020): 2050003. http://dx.doi.org/10.1142/s2424922x20500035.

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Highway-rail grade crossing (HRGC) accidents continue to be a major source of transportation casualties in the United States. This can be attributed to increased road and rail operations and/or lack of adequate safety programs based on comprehensive HRGC accidents analysis amidst other reasons. The focus of this study is to predict HRGC accidents in a given rail network based on a machine learning analysis of a similar network with cognate attributes. This study is an improvement on past studies that either attempt to predict accidents in a given HRGC or spatially analyze HRGC accidents for a

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Kwak, Geun-Ho, and No-Wook Park. "Impact of Texture Information on Crop Classification with Machine Learning and UAV Images." Applied Sciences 9, no. 4 (2019): 643. http://dx.doi.org/10.3390/app9040643.

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Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are

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Butnaru, Andrei-Mădălin. "Machine learning applied in natural language processing." ACM SIGIR Forum 54, no. 1 (2020): 1–3. http://dx.doi.org/10.1145/3451964.3451979.

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Machine Learning is present in our lives now more than ever. One of the most researched areas in machine learning is focused on creating systems that are able to understand natural language. Natural language processing is a broad domain, having a vast number of applications with a significant impact in society. In our current era, we rely on tools that can ease our lives. We can search through thousands of documents to find something that we need, but this can take a lot of time. Having a system that can understand a simple query and return only relevant documents is more efficient. Although c

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Sandhu, Sahil, Anthony L. Lin, Nathan Brajer, et al. "Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study." Journal of Medical Internet Research 22, no. 11 (2020): e22421. http://dx.doi.org/10.2196/22421.

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Background Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. Objective This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. Methods We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participate

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Kobsar, Dylan, and Reed Ferber. "Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach." Sensors 18, no. 9 (2018): 2828. http://dx.doi.org/10.3390/s18092828.

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Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill

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Li, Jiaxin, Zijun Zhou, Jianyu Dong, et al. "Predicting breast cancer 5-year survival using machine learning: A systematic review." PLOS ONE 16, no. 4 (2021): e0250370. http://dx.doi.org/10.1371/journal.pone.0250370.

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Background Accurately predicting the survival rate of breast cancer patients is a major issue for cancer researchers. Machine learning (ML) has attracted much attention with the hope that it could provide accurate results, but its modeling methods and prediction performance remain controversial. The aim of this systematic review is to identify and critically appraise current studies regarding the application of ML in predicting the 5-year survival rate of breast cancer. Methods In accordance with the PRISMA guidelines, two researchers independently searched the PubMed (including MEDLINE), Emba

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Johri, Prashant, Vivek sen Saxena, and Avneesh Kumar. "Rummage of Machine Learning Algorithms in Cancer Diagnosis." International Journal of E-Health and Medical Communications 12, no. 1 (2021): 1–15. http://dx.doi.org/10.4018/ijehmc.2021010101.

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With the continuous improvement of digital imaging technology and rapid increase in the use of digital medical records in last decade, artificial intelligence has provided various techniques to analyze these data. Machine learning, a subset of artificial intelligence techniques, provides the ability to learn from past and present and to predict the future on the basis of data. Various AI-enabled support systems are designed by using machine learning algorithms in order to optimize and computerize the process of clinical decision making and to bring about a massive archetype change in the healt

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Dutta, P. S., N. R. Jennings, and L. Moreau. "Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems." Journal of Artificial Intelligence Research 24 (October 1, 2005): 407–63. http://dx.doi.org/10.1613/jair.1735.

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Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information bet

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