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RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

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Journal of Intelligent & Robotic systems 2015年第2期82卷 177-187页

作者： Petković, Dalibor Shamshirband, Shahaboddin Anuar, Nor Badrul Sabri, Aznul Qalid Md Abdul Rahman, Zulkanain Bin Pavlović, Nenad D. Faculty of Mechanical Engineering Deparment for Mechatronics and Control University of Niš Niš Serbia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of History Faculty of Arts and Social Sciences Building University of Malaya Kuala Lumpur Malaysia

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

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Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000-17: analysis for the Global Burden of Disease Study 2017 (vol 395, pg 1779, 2020)

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LANCET 2020年第10239期395卷 1762-1762页

作者： Reiner, R. C., Jr. Hay, S., I Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Global Health School of Medicine University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States College of Health and Medical Sciences Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics Haramaya University Harar Ethiopia Department of Medical Laboratory Sciences Haramaya University Harar Ethiopia School of Nursing and Midwifery Haramaya University Harar Ethiopia School of Pharmacy Haramaya University Harar Ethiopia School of Public Health Haramaya University Harar Ethiopia Haramaya University Harar Ethiopia Advanced Diagnostic and Interventional Radiology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Biology Research Center Tehran University of Medical Sciences Tehran Iran Cancer Research Institute Tehran University of Medical Sciences Tehran Iran Department of Economics and Management Sciences for Health Tehran University of Medical Sciences Tehran Iran Department of Environmental Health Engineering Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Department of Microbiology Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Hematology-Oncology and Stem Cell Transplantation Research Center Tehran University of Medical Sciences Tehran Iran Iran National Institute of Health Research Tehran University of Medical Sciences Tehran Iran Metabolomics and

SummaryBackgroundAcross low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood diarrhoea. MethodsWe used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor estimates. FindingsThe greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1–65·8), 17·4% (7·7–28·4), and 59·5% (34·2–86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy coverage. InterpretationBy co-analysing geospatial trends in dia

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Segmentation of solid subregion of high grade gliomas in MRI images based on active contour model (ACM)

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Journal of Physics: Conference Series 2016年第1期694卷

作者： P Seow M T Win J H D Wong N A Abdullah N Ramli Department of Biomedical Imaging Faculty of Medicine University of Malaya Kuala Lumpur Malaysia University of Malaya Research Imaging Centre Faculty of Medicine University of Malaya Kuala Lumpur Malaysia Department of System and Computer Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

Gliomas are tumours arising from the interstitial tissue of the brain which are heterogeneous, infiltrative and possess ill-defined borders. Tumour subregions (e.g. solid enhancing part, edema and necrosis) are often used for tumour characterisation. Tumour demarcation into substructures facilitates glioma staging and provides essential information. Manual segmentation had several drawbacks that include laborious, time consuming, subjected to intra and inter-rater variability and hindered by diversity in the appearance of tumour tissues. In this work, active contour model (ACM) was used to segment the solid enhancing subregion of the tumour. 2D brain image acquisition data using 3T MRI fast spoiled gradient echo sequence in post gadolinium of four histologically proven high-grade glioma patients were obtained. Preprocessing of the images which includes subtraction and skull stripping were performed and then followed by ACM segmentation. The results of the automatic segmentation method were compared against the manual delineation of the tumour by a trainee radiologist. Both results were further validated by an experienced neuroradiologist and a brief quantitative evaluations (pixel area and difference ratio) were performed. Preliminary results of the clinical data showed the potential of ACM model in the application of fast and large scale tumour segmentation in medical imaging.

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Robot Communication based on Relational Trust Model

Robot Communication based on Relational Trust Model

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Annual Conference of the IEEE Industrial Electronics Society

作者： Saika Ono Takenori Obo Loo Chu Kiong Naoyuki Kubota Graduate School of System Design Tokyo Metropolitan University Faculty of Computer Science and Information Technology University of Malaya

ISBN: (纸本)9781479917631

In this study, we aim to develop a system for improving daily lives of elderly people to ensure health. In order to realize an enriched life style among elderly people, daily health care is important. Therefore, we have proposed a system where robot partners will assist in exercising activity among elderly people. This paper proposes a method of relational trust modeling based on reinforcement learning. We apply a concept of relational trust defined as the expectation that a person is disposed to act in a trustworthy manner toward "me," no matter what the person does to others. In the experiment, we discuss the effectiveness of relational trust for robot communication.

关键词： Robot Partner Communication Relational Trust Reinforcement Learning Elderly People Care Communication Research learning (artificial intelligence) aged person Nonprofit Organizations Trust trust Persons Robot Life Style Learning

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Multi-channel Bayesian adaptive resonance associative memory for environment learning and topological map building

Multi-channel Bayesian adaptive resonance associative memory...

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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Wei Hong Chin Chu Kiong Loo Naoyuki Kubota Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of System Design Tokyo Metropolitan University Tokyo Japan

This paper presents a new network for environment learning and online topological map building. It comprises two layers: input and memory. The input layer collects sensory information and incrementally categorizes the obtained information into a set of topological nodes. In the memory layer, edges are connect clustered information (nodes) to form a topological map. Edges store robot's actions and bearing. The advantages of the proposed method are: 1) it represents multiple places using multidimensional Gaussian distribution and does not require prior knowledge to make it work in a natural environment; 2) it can process more than one sensory source simultaneously in continuous space during robot navigation; and 3) it is an incremental and using Bayes' decision theory for learning and inference. Finally, the proposed method was validated using several standardized benchmark datasets.

关键词： Robot sensing systems Bayes methods Buildings Robot kinematics Navigation Measurement

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DIAS: A disassemble-assemble framework for highly sparse text clustering

DIAS: A disassemble-assemble framework for highly sparse tex...

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SIAM International Conference on Data Mining 2015, SDM 2015

作者： Liu, Hongfu Wu, Junjie Tao, Dacheng Zhang, Yuchao Fu, Yun Department of Electrical and Computer Engineering College of Engineering College of Computer and Information Science Northeastern University Boston United States School of Economics and Management Beihang University Beijing China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia Beijing Institute of System Engineering Beijing China

ISBN: (纸本)9781510811522

Upon extensive studies, text clustering remains a critical challenge in data mining community. Even by various techniques proposed to overcome some of these challenges, there still exist problems when dealing with weakly related or even noisy features. In response to this, we propose a Dlssemble-ASsemble (DIAS) framework for text clustering. DIAS employs simple random feature sampling to disassemble high-dimensional text data and gains diverse structural knowledge. This also does good to avoiding the bulk of noisy features. Then the multi-view knowledge is assembled by weighted Information-theoretic Consensus Clustering (IC-C) in order to gain a high-quality consensus partitioning. Extensive experiments on eight real-world text data sets demonstrate the advantages of DIAS over other widely used methods. In particular, DIAS shows strengths in learning from very weak basic partitionings. In addition, it is the natural suitability to distributed computing that makes DIAS become a promising candidate for big text clustering. Copyright © SIAM.

关键词： Distributed computer systems

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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RETRACTED ARTICLE: Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model

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Theoretical and Applied Climatology 2015年第3期125卷 679-689页

作者： Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Khorasanizadeh, Hossein Seyed Danesh, Amir Piri, Jamshid Ismail, Zuraini Zamani, Mazdak Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Mechanical Engineering University of Kashan Kashan Iran Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Water Engineering Soil and Water College University of Zabol Zabol Iran Advanced Informatics School Universiti Teknologi Malaysia Johor Bahru Malaysia

The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively.

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RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

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Soft Computing 2015年第10期20卷 4069-4084页

作者： Saybani, Mahmoud Reza Wah, Teh Ying Aghabozorgi, Saeed Reza Shamshirband, Shahaboddin Mat Kiah, Miss Laiha Balas, Valentina Emilia Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Kula Lumpur Malaysia Department of Computer Networks Markaz-e Elmi Karbordi Bandar Abbas 1 University of Applied Science Bandar Abbas Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad Romania

Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student’s t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

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RETRACTED ARTICLE: OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises

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Annals of Operations Research 2015年第1期229卷 743-758页

作者： Shahaboddin Shamshirband Mohammad Shojafar A. A. Rahmani Hosseinabadi Maryam Kardgar M. H. N. Md. Nasir Rodina Ahmad Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Rome Italy Young Research Club Behshahr Branch Islamic Azad University Behshahr Iran Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya (UM) Kuala Lumpur Malaysia

The problem of open-shop scheduling includes a set of activities which must be performed on a limited set of machines. The goal of scheduling in open-shop is the presentation of a scheduled program for performance of the whole operation, so that the ending performance time of all job operations will be minimised. The open-shop scheduling problem can be solved in polynomial time when all nonzero processing times are equal, becoming equivalent to edge coloring that has the jobs and workstations as its vertices and that has an edge for every job-workstation pair with a nonzero processing time. For three or more workstations, or three or more jobs, with varying processing times, open-shop scheduling is NP-hard. Different algorithms have been presented for open-shop scheduling so far. However, most of these algorithms have not considered the machine maintenance problem. Whilst in production level, each machine needs maintenance, and this directly influences the assurance reliability of the system. In this paper, a new genetic-based algorithm to solve the open-shop scheduling problem, namely OSGA, is developed. OSGA considers machine maintenance. To confirm the performance of OSGA, it is compared with DGA, SAGA and TSGA algorithms. It is observed that OSGA performs quite well in terms of solution quality and efficiency in small and medium enterprises (SMEs). The results support the efficiency of the proposed method for solving the open-shop scheduling problem, particularly considering machine maintenance especially in SMEs’.

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