Read by QxMD icon Read

Problem based learning

Beatrix Fahnert
Innovative practice from around the globe, addressing a range of recent educational themes and trends, was published in the FEMS Microbiology Letters virtual Thematic Issue ''Keeping Education Fresh' in October 2017. Its thought-provoking content is reviewed here to more directly facilitate reflections and discussions in the professional community. The focus is on best practice approaches when enhancing student engagement, how to adjust those to the diversity of learners, learning situations and infrastructures, and to a broad range of subjects...
October 4, 2017: FEMS Microbiology Letters
Josey Mathew, Chee Khiang Pang, Ming Luo, Weng Hoe Leong
Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and consist of multiple categories or classes. Learning discriminative models from such data sets is challenging due to the lack of representative data and the bias of traditional classifiers toward the majority class. Sampling methods like synthetic minority oversampling technique (SMOTE) have been traditionally used for such problems to artificially balance the data set before being trained by a classifier. This paper proposes a weighted kernel-based SMOTE (WK-SMOTE) that overcomes the limitation of SMOTE for nonlinear problems by oversampling in the feature space of support vector machine (SVM) classifier...
October 10, 2017: IEEE Transactions on Neural Networks and Learning Systems
Yuguang Yan, Qingyao Wu, Mingkui Tan, Michael K Ng, Huaqing Min, Ivor W Tsang
In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data...
October 10, 2017: IEEE Transactions on Neural Networks and Learning Systems
Gregory Ditzler, Joseph LaBarck, James Ritchie, Gail Rosen, Robi Polikar
Feature subset selection can be used to sieve through large volumes of data and discover the most informative subset of variables for a particular learning problem. Yet, due to memory and other resource constraints (e.g., CPU availability), many of the state-of-the-art feature subset selection methods cannot be extended to high dimensional data, or data sets with an extremely large volume of instances. In this brief, we extend online feature selection (OFS), a recently introduced approach that uses partial feature information, by developing an ensemble of online linear models to make predictions...
October 11, 2017: IEEE Transactions on Neural Networks and Learning Systems
Wenqing Chu, Yao Liu, Chen Shen, Deng Cai, Xian-Sheng Hua
Vehicle detection is a challenging problem in autonomous driving systems, due to its large structural and appearance variations. In this paper, we propose a novel vehicle detection scheme based on multi-task deep convolutional neural networks (CNN) and region-of-interest (RoI) voting. In the design of CNN architecture, we enrich the supervised information with subcategory, region overlap, bounding-box regression and category of each training RoI as a multi-task learning framework. This design allows the CNN model to share visual knowledge among different vehicle attributes simultaneously, thus detection robustness can be effectively improved...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Eunhee Kang, Junhong Min, Jong Chul Ye
PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns...
October 2017: Medical Physics
He Len Chung, André Monday, Alexus Perry
OBJECTIVES: The aim of this study was to examine youths' perceptions of a drama-based peer education approach to promote adolescent well-being. METHODS: High school students facilitated workshops on one of 7 topics (eg, dating violence) for 4733 urban elementary, middle, and high school students. Audience members' perceptions of workshop content and implementation were examined. RESULTS: Analyses suggest that the peer-led workshops addressed important problems for youth in the community, were an effective way to deliver information about the topics to children and teens, and helped audience members to learn more about the topics and how to make responsible decisions...
November 1, 2017: American Journal of Health Behavior
S L Stewart, K Falah Hassani, J Poss, J Hirdes
BACKGROUND: To date, little is known about the predictors of healthcare service utilisation in children with intellectual disability (ID). The aim of this study was to identify the factors associated with service complexity in children with ID in Ontario, Canada. METHODS: The population of this cross-sectional study consisted of 330 children with ID ages 4 to 18 years who accessed mental health services from November of 2012 to June of 2016 in four agencies. All participants completed the interRAI Child and Youth Mental Health and Developmental Disability Assessment Instrument, which is a semi-structured clinician-rated assessment that covers a range of common issues in children with ID...
November 2017: Journal of Intellectual Disability Research: JIDR
Fan-Shu Chen, Hui-Yan Jiang, Zhenran Jiang
Prediction of new associations between drugs and targeting pathways can provide valuable clues for drug discovery & development. However, information integration and a class-imbalance problem are important challenges for available prediction methods. This paper proposes a prediction of potential associations between drugs and pathways based on a disease-related LSA-PU-KNN method. Firstly, we built a drug-disease-pathway network and combined the drug-disease and pathway-disease features obtained by different types of feature profiles...
October 12, 2017: Molecular BioSystems
Michael P Pound, Jonathan A Atkinson, Alexandra J Townsend, Michael H Wilson, Marcus Griffiths, Aaron S Jackson, Adrian Bulat, Georgios Tzimiropoulos, Darren M Wells, Erik H Murchie, Tony P Pridmore, Andrew P French
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power...
October 1, 2017: GigaScience
Jerry W Sayre, Hale Z Toklu, Fan Ye, Joseph Mazza, Steven Yale
Case reports and case series or case study research are descriptive studies that are prepared for illustrating novel, unusual, or atypical features identified in patients in medical practice, and they potentially generate new research questions. They are empirical inquiries or investigations of a patient or a group of patients in a natural, real-world clinical setting. Case study research is a method that focuses on the contextual analysis of a number of events or conditions and their relationships. There is disagreement among physicians on the value of case studies in the medical literature, particularly for educators focused on teaching evidence-based medicine (EBM) for student learners in graduate medical education...
August 7, 2017: Curēus
Gokmen Zararsiz, Dincer Goksuluk, Bernd Klaus, Selcuk Korkmaz, Vahap Eldem, Erdem Karabulut, Ahmet Ozturk
RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data...
2017: PeerJ
Khalid Masood Gondal, Uzma Iqbal, Arslan Ahmed, Junaid Sarfraz Khan
OBJECTIVE: To find out the perspective of the supervisors about the role of electronic logbook (E-Logbook) of College of Physicians and Surgeons, Pakistan (CPSP) in monitoring the training of postgraduate medical residents of CPSP. STUDY DESIGN: Descriptive cross-sectional study. PLACE AND DURATION OF STUDY: College of Physicians and Surgeons Pakistan (CPSP), Karachi, from May to October 2015. METHODOLOGY: An electronic computer-based questionnaire designed in Hypertext Preprocessor (PHP) language was distributed to the registered CPSPsupervisors through the e-log system...
September 2017: Journal of the College of Physicians and Surgeons—Pakistan: JCPSP
Anna Kiessling, Martin Roll, Peter Henriksson
BACKGROUND: A hospital with all its brimming activity constitutes a unique learning environment for medical students. However, to organise high-quality education within this context is a task of great complexity. This paper describes a teaching hospital case, where management principles were applied to enhance the learning quality of medical education. METHODS: Traditional attempts from the faculty had been unsuccessful in improving learning among medical students at a teaching hospital...
October 10, 2017: BMC Medical Education
Maria Taboada, Hadriana Rodriguez, Ranga C Gudivada, Diego Martinez
BACKGROUND: Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem. RESULTS: This paper presents an automated synonym-substitution method to enrich the Human Phenotype Ontology (HPO) with new synonyms...
October 10, 2017: BMC Bioinformatics
Shuanghui Zhang, Yongxiang Liu, Xiang Li
Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data. To deal with this problem, this paper proposes a novel SA-InISAR imaging method, which jointly reconstructs 2-dimensional (2-D) ISAR images from different channels through multiple response sparse Bayesian learning (M-SBL), a modification of sparse Bayesian learning (SBL), to achieve sparse recovery for multiple measurement vectors (MMV)...
October 10, 2017: Sensors
Zheng Wang, Ruimin Hu, Chen Chen, Yi Yu, Junjun Jiang, Chao Liang, Shin'ichi Satoh
Person reidentification (re-id), as an important task in video surveillance and forensics applications, has been widely studied. Previous research efforts toward solving the person re-id problem have primarily focused on constructing robust vector description by exploiting appearance's characteristic, or learning discriminative distance metric by labeled vectors. Based on the cognition and identification process of human, we propose a new pattern, which transforms the feature description from characteristic vector to discrepancy matrix...
October 5, 2017: IEEE Transactions on Cybernetics
Zhihui Lai, Dongmei Mo, Wai Keung Wong, Yong Xu, Duoqian Miao, David Zhang
Ridge regression (RR) and its extended versions are widely used as an effective feature extraction method in pattern recognition. However, the RR-based methods are sensitive to the variations of data and can learn only limited number of projections for feature extraction and recognition. To address these problems, we propose a new method called robust discriminant regression (RDR) for feature extraction. In order to enhance the robustness, the L₂,₁-norm is used as the basic metric in the proposed RDR. The designed robust objective function in regression form can be solved by an iterative algorithm containing an eigenfunction, through which the optimal orthogonal projections of RDR can be obtained by eigen decomposition...
October 9, 2017: IEEE Transactions on Cybernetics
Hongjie Wu, Chengyuan Cao, Xiaoyan Xia, Qiang Lu
Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences...
October 9, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Il Yong Chun, Jeffrey A Fessler
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems...
October 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"