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2007 IADIS European Conference on Data Mining, DM 2007, part of the 1st IADIS Multi Conference on computer Science and Information Systems, MCCSIS 2007

作者： Bouras, Christos Poulopoulos, Vassilis Tsogkas, Vassilis Research Academic Computer Technology Institute Computer Engineering and Informatics Department University of Patras N. Kazantzaki Str. Rion26500 Greece

ISBN: (纸本)9789728924409

The amount of data that exists on the Internet is enormous enough to distract users when trying to find useful information. In addition, the expansive use of small screen devices for browsing the World Wide Web generates huge problems when trying to find and read information. A solution to these problems is to personalize the web and try to reduce algorithmically the amount of text. Many text summarizers have been presented in order to reduce the valueless information that is presented to the users and many web sites, especially news portals, introduce personalization features for the users, though, still these techniques are not often used in combination in order to create more effective results. In this paper a mechanism for creating personalized summaries for the members of a news portal that reproduces articles collected from major portals is presented, together with evaluation both of the summarizer and the personalized summaries. The evaluators of the personalized summaries are members of the news portal. The personalized summary mechanism can also be utilized by users of small screen devices, for easier reading of less but inclusive information. © 2007 IADIS

关键词： Text processing

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Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

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ACS Chemical Biology 2023年第3期18卷 476-483页

作者： Jian, Bo-Siyuan Chiou, Shao-Lun Hsu, Chun-Chia Ho, Josh Wu, Yu-Wei Chu, John Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 10675 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 10675 Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei 10675 Taiwan

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms. © 2023 The Authors. Published by American Chemical Society.

关键词： Algorithms Bacteria Bioinformatics and computational biology Peptides and proteins Pharmaceuticals

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Online Affinity of Instructing Methods for Using Personal-Efficacy and Reading Capacious During Covid-19 5th

Online Affinity of Instructing Methods for Using Personal-Ef...

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5th International Conference on Communication, Device and Networking, ICCDN 2021

作者： PremaLatha, V. Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Bhoi, Akash Kumar Ghosh, Uttam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Guntur Vaddeswaram522502 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur603203 India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India Department of Computer Science and Data Science Meharry School of Computational Sciences Nashville NashvilleTN United States

ISBN: (纸本)9789811920035

Now a days in EFL procedure of education the ability of reading became as significant belief and personal-efficacy reading as a basic understanding for students. By monitoring the acknowledged participates under the ballpark figure of large studying and methods of understanding, the impact of their observation is premeditated on reading of each one’s personal-efficacy. On a daily routine all these things are comparatively considered which are put into effect by teachers of handful in number. Approach towards exhibiting Extensive reading (ER) is inspected to be "more expensive, difficult, and time-consuming". Method of recognition of elements in a various way for effective impact in putting its efforts to utilize for its empowerment. Paper has been segregated into two contexts: Association with attitude is considered as primary one and attitude is considered as secondary one. Whether knowledge work is understood by student or not is considered as the impact of ER by the first review. Procedure which are convenient is taken as the observations of student and is analysed as second one. The examinations are quantifiable to utilize the observations as information in terms of subjective way taken from students who belong to first academic year of reading course in a systematic way and 603 details of undergraduate students from KLEF of Guntur were chosen as participants for extant examination. In ER programme of includes and excludes "comprehension reading work" is treated as fundamental in the proposal of disclosures. In case of any, "the programme appeared to positively affect contributing students". Techniques of classification like "decision tree and Mixed Model Database Miner (MMDBM)" are employed in this paper which leads to improvements of post-test to pre-test in ER group. Observations of students in ER results as optimistic and algorithm of MMDBM which leads to accuracy in higher rate in pre-test and post-test detection. © 2023, The Author(s), under exclusive lice

关键词： Students

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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The Calibration of Single Beam Distance Sensors based on Machine Learning Methods

The Calibration of Single Beam Distance Sensors based on Mac...

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IEEE International Conference on Big Data

作者： Piotr Biernacki Adam Ziebinski Jia-Hao Syu Jerry Chun-Wei Lin Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors.

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Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-based Classifier

Detection of Chronic Kidney Disease Using Neuro-Fuzzy Rule-b...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Das, Supantha Hazra, Arnab Pati, Soumen Kumar Ghosh, Soumadip Mallik, Saurav Banerjee, Suharta Mukherji, Ayan Li, Aimin Zhao, Zhongming Academy of Technology Hooghly Department of Information Technology West Bengal India Future Institute of Technology Kolkata Department of Computer Science and Engineering West Bengal India Maulana Abul Kalam Azad University of Technology West Bengal Haringhata Department of Bioinformatics West Bengal Nadia India Sister Nivedita University Department of Computer Science and Engineering West BengalNew Town Kolkata India Harvard T H Chan School of Public Health Department of Environmental Health BostonMA United States Meghnad Saha of Institute of Technology Department of Computer Science and Engineering West Bengal Kolkata India Pailan College of Management & Technology Department of Computer Science and Engineering West Bengal Kolkata India Xi'an University of Technology School of Computer Science & Engineering Shaanxi Xi'an710048 China The University of Texas Health Science Center Center for Precision Health School of Biomedical Informatics HoustonTX77030 United States

ISBN: (纸本)9781665468190

Chronic kidney disease (CKD) is as severe as cancer in today s world. It may even lead to the permanent failure of kidney. The initial detection of this disease is needed for timely cure. Our work presents a classifier (named ANFIS) in accordance with the notion of neuro-fuzzy in order to detect the existence of chronic kidney disease. We use blood test results of several patients for our research study. We compare our proposed classifier with some conventional classifiers such as Multi-layer Perceptron, Support Vector Machine, Logistic Regression and Decision Tree. Experimental results indicates that our proposed neuro-fuzzy rule-based classifier performs better than the other classifiers used here. ANFIS has given 3% to 4% better accuracy compared to the other classifiers. © 2022 IEEE.

关键词： Decision trees

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Implementation of Machine Learning and Ensemble Learning Techniques to Proactively Predict Students' academic Success in the Realm of Higher Education

Implementation of Machine Learning and Ensemble Learning Tec...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Taneja Sanjay Devkishan Sanjay Kumar Singh Ajay Kumar Bharti Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Predicting and analyzing student performance is a huge boon for both students and teachers. A number of analytical methodologies have been utilized in recent studies to forecast students' success; the CGPA is the end product of these analyses, which have been conducted for both the internal assessment as well as for the external assessment. Few well known machine learning algorithms have been suggested. Ensemble Learning along with Decision Trees, Support Vector Machine and Random Forest -obtained perfect 100s for Precision Value, Recall Value, as well as in F1 Scores in the evaluation of performances. Naive Bayes has worse performance in comparison, with F1 Scores of 66, Recall Value of 65, and Precision of 68. When contrasted with decision tree models, Support Vector Machines, Multi-Layer Perceptron, Ensemble Enabled Learning, and Random Forest, Naive Bayes, AdaBoost, and K-Nearest Neighbors all perform worse in terms of accuracy. The Naive Bayes classifier models the input distributions across classes and is very good in text categorization because it is a generative method. Since it fails to identify critical features for class differentiation, it cannot be distinguished from discriminative classifiers.

关键词： Support vector machines Accuracy Text categorization Supervised learning Predictive models Nearest neighbor methods Nonhomogeneous media Decision trees Ensemble learning Random forests

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Sensor Based Smart Wearable Devices for Women: A Review

Sensor Based Smart Wearable Devices for Women: A Review

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chinmaya Khadse Shital Hajare Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Natural organic menstrual cycles stay in large part taboo during many regions of India mainly rural areas hence threatening teenagers with useless menstrual hygiene requirements mixed with primary sanitation issues and network disapproval. The fitness problems and psychological misery get up from these subjects. The World Health Organization (WHO) establishes that inadequate menstrual hygiene ends in urinary tract infections and infections of the reproductive device. The importance of India's menstrual population now stands at 355 million girls and ladies which needs immediately reaction through powerful answers. This observe investigates how IA and IoT-driven smart wearable generation can increase menstrual health control (MHM) for teenage Indian women. A proposed low cost wearable tool collects basal body temperature (BBT) and heart charge variability (HRV) information and bodily activity information while retaining cultural appropriateness. AI chatbots and academic guide and actual-time comments capabilities exist inside this device. The system features complete menstrual health tracking competencies with the aid of using cycle prediction competencies and implements in-tool heating structures for ache discount together with strain reduction software for emotional aid. The system uses techniques that start from person desires even as imposing LSTM networks together with Random Forest Regression machines for forecasting menstrual cycles and symptom reviews. Validations run on the tool arise in practical conditions which observe both its performance accuracy along person pleasure quotes and reliability through the years. The observe shows wearable era has the capacity to revolutionize menstruation take care of Indian teens via presenting them with a powerful approach to address their public health needs.

关键词： Temperature measurement Heating systems Reviews Internet of Things Biomedical monitoring Wearable devices Heart rate variability Random forests Long short term memory Faces

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