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Classification of Mild Cognitive Impairment stages using Machine learning Methods

Classification of Mild Cognitive Impairment stages using Mac...

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IEEE international conference on intelligent engineering Systems

作者： Ylermi Cabrera-Leon Patricio Garcia Baez Juan Ruiz-Alzola Carmen Paz Suarez-Araujo Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Canary Islands Spain Departamento de Ingeniería Informática y de Sistemas Universidad de La Laguna San Cristóbal de La Laguna Canary Islands Spain Instituto Universitario de Investigaciones Biomédicas y Sanitarias Universidad de Las Palmas de Gran Canaria Las Palmas de Gran Canaria Canary Islands Spain Faculty of Engineering and Information Technology University of Technology Sydney (UTS)

The elderly population in developed countries has augmented in the last decades. This has also entailed an increased prevalence of aging diseases. Mild Cognitive Impairment (MCI) is considered a prodromal stage of Alzheimer's Disease (AD), which is the most common dementia. We present a benchmarking of Machine learning (ML) methods for MCI staging and its early detection via the multiclass classification of Cognitively Normal Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCT) subjects by means of neuropsychological scales. data were obtained from l/heimer's Disease Neuroimaging Initiative (ADM). Our stud) has been aimed towards hybrid neural architectures, such as the Counterpropagation Network (CPN), for their input space quantification capability, and non-neural methods including ensembles. We also analyzed how class balancing affected them. Non-neural ensembles of learners obtained Area Under the Curve (AUC) values in the range 0.721 0 775, whereas 0 650 0 657 with the monolithic architecture CPN. This suggests that there is a weak difference among these cognitive stages, that the used scales do not offer high enough discriminant power, and that neural ensembles can be a more appropriate solution.

关键词： Training Alzheimer's disease Neuroimaging Self-organizing feature maps Neurons

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Application layer channel coding for space DTN 2nd

Application layer channel coding for space DTN

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2nd international conference on Machine learning and intelligent Communications, MLICOM 2017

作者： Hou, Dongxu Zhao, Kanglian Li, Wenfeng School of Electronic Science and Engineering Nanjing University 22 Hankou Rd NanjingJiangsu210093 China

ISBN: (纸本)9783319735634

Space communications have the characteristics of long link delays, frequent link disruptions and high error rates. With reliable Licklider Transmission Protocol (LTP) or Transmission Control Protocol (TCP), automatic repeat request (ARQ) is applied to enable reliable data delivery in delay/disruption tolerant networking (DTN). However, ARQ is inefficient for space communications especially in links with long round trip time (RTT). In this paper, an application layer Reed-Solomon (ALRS) channel coding scheme is proposed, which is further combined with ARQ to guarantee reliable transmission in DTN architecture. The proposed ALRS coding scheme is implemented in open source ION-DTN software and its performance is evaluated on a dedicated testbed. The results of the experiments show that this scheme in DTN can be speed up in most scenarios compared with ARQ-only scheme. With coding in application layer, the scheme is also more compatible with the overlay characteristic of DTN. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2018.

关键词： Channel coding

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Methodology for Attention Detection based on Heart Rate Variability

Methodology for Attention Detection based on Heart Rate Vari...

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IEEE international conference on intelligent engineering Systems

作者： A. Artifice J. Sarraipa R. Jardim-Goncalves DEE Universidade Nova de Lisboa Caparica Portugal

This work proposes a methodology to measure attention. The proposed methodology, based on Heart Kate Variability (HRV), is composed by the following phases: pre-processing, feature extraction and data analytics. During pre-processing stage, the electrocardiogram (ECG) signal is filtered to remove noise from the signal and HRV signal from ECG is computed. In the feature extraction phase are computed the 12 features for HRV signal description based on linear methods. This 12 linear features include both features from time domain and from frequency domain. data analytics step is responsible to analyze both the spectral power in the high-frequency (HF) band of the HRV signal and low frequency (LF) band. The proposed methodology was tested in a game playing scenario. Such scenario consists of playing game in two distinct circumstances: playing a game with background classic facilitator music, then with annoying music. The analysis of HF and LF parameters revealed a decrease in determined moments of the experience, which is aligned with a study arguing that those parameters decrease in attentional tasks. In this work variations of those parameters were correlated with the players perception of their attention.

关键词： Heart rate variability Feature extraction data analysis Frequency-domain analysis Electrocardiography Time-domain analysis Task analysis

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Hybrid approach towards the assessment of a drill condition using deep learning and the Support Vector Machine

Hybrid approach towards the assessment of a drill condition ...

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international Computer Science and engineering conference (ICSEC)

作者： Jaroslaw Kurek Faculty of Applied Informatics and Mathematics Warsaw University of Life Sciences Warsaw Poland

ISBN: (纸本)9781538681657;9781538681640

This paper describes an application of a novel method which relies on applying the fusion of two sets of features obtained from deep learning and hand crafted features. Moreover, in terms of deep learning, transfer learning in deep learning has been applied. A hybrid method has been compared to classical deep learning approach, and let us state that the new approach can obtain more accuracy than regular methods. Transfer learning in deep learning should be used in case of lack of data to train the use of deep learning algorithms. We have used this method for the classification of drill wear state on the basis of drilled hole images. The specialists divided the whole data set into three classes: red, yellow, and green, which correspond to 3 stages of drill wear. The red class corresponds to a drill which is worn out and should be replaced; the yellow class should send a warning message to an operator for a manual check of the state of a drill; and the last one, the green one, is connected with the state of a drill which should be further used in production (it is still sharp enough). The important advantage of this approach is that it collects features from two sources: one set is automatically extracted by means of deep learning and the other is manually extracted by a researcher. The next advantage of this approach is training classification models only on the basis of a small portion of data, which in case of popular deep learning methods is too small to achieve reasonable accuracy. Hence, in order to get around this issue connected with a small portion of training data, transfer learning in deep learning has been applied. The achieved results confirm the fact that this approach can be applied in this situation.

关键词： Feature extraction Deep learning Neurons Training Drilling machines Filtering

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intelligent Immune System for Sustainable Manufacturing

Intelligent Immune System for Sustainable Manufacturing

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international conference on Computer Supported Cooperative Work in Design

作者： W.D. Li X.T. Cai School of Logistics Engineering Wuhan University of Technology Wuhan P.R. China School of Computer Science and Engingeering Wuhan University Wuhan P.R. China

ISBN: (纸本)9781538614839

An innovative Big data enabled intelligent Immune System (1 2 S) has been developed to address manufacturing dynamics over life cycles to achieve sustainable manufacturing. The 1 2 S is a novel inter-disciplinary integrated system of the artificial immune mechanism, the Wireless Sensor Network (WSN)-based Cyber Physical System (CPS), Artificial Neural Networks (ANNs) and sustainable manufacturing scheduling optimization algorithm. Abnormal energy consumption patterns of manufactured components from monitored Big data are identified using ANNs. An intelligent immune mechanism is devised to adapt to the pattern/condition changes of machine tool systems and process dynamics. A rescheduling algorithm is triggered if abnormal manufacturing conditions are detected thereby achieving adaptive multiobjective optimization of energy consumption and manufacturing performance. Computer Numerical Controlled (CNC) machining processes have been used for system validation via industrial deployment into multiple machine lines in manufacturing factories. Around 30% in energy saving and over 50% in productivity improvement have been achieved by adopting 1 2 S.

关键词： Manufacturing Machining Energy consumption Optimization Immune system Monitoring Big data

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Predictive team formation analysis via feature representation learning on social networks 22nd

Predictive team formation analysis via feature representatio...

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22nd Pacific-Asia conference on Advances in Knowledge Discovery and data Mining, PAKDD 2018

作者： Ting, Lo Pang-Yun Li, Cheng-Te Chuang, Kun-Ta Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Institute of Data Science National Cheng Kung University Tainan Taiwan Department of Statistics National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9783319930398

Team formation is to find a group of experts covering required skills and well collaborating together. Existing studies suffer from two defects: cannot afford flexible designation of team members and do not consider whether the formed team is truly adopted in practice. In this paper, we propose the Predictive Team Formation (PTF) problem. PTF provides the flexibility of designated members and delivers the prediction-based formulation to compose the team. We propose two methods by learning the feature representations of experts based on node2vec [4]. One is Biased-n2v that models the topic bias of each expert in the social network. The other is Guided-n2v that refines the transition probabilities between skills and experts to guide the random walk in a heterogeneous graph of expert-expert, expert-skill, and skill-skill. Experiments conducted on DBLP and IMDb datasets exhibit that our methods can significantly outperform the state-of-the-art optimization-based approaches in terms of prediction recall. We also reveal that the designated members with tight social connections can lead to better performance. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： learning systems

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Word Recognition in Captured Images by CNN Trained with Synthetic Images

Word Recognition in Captured Images by CNN Trained with Synt...

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international Computer Science and engineering conference (ICSEC)

作者： Arthicha Srisuchinnawong Bawornsak Sakulkueakulsuk Warasinee Chaisangmongkon Institute of Field Robotics King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (纸本)9781538681657;9781538681640

Problems like robotic navigation and automatic geocoding of businesses require artificial agents to perform rapid and accurate word recognition in natural images. We set out to develop a deep learning method to recognize words from different languages in captured images, with high accuracy and with small number of captured samples. Our experiments reveal three main findings. First, we feed images of words as inputs to the neural network directly, omitting segmentation and postprocessing step to avoid compound errors. We found this method to work well for our samples. Second, we are able to train machine learning models to recognize words using purely synthetic training samples by applying feature extractions to both training and testing datasets prior to passing them through deep networks. This achievement allows us to train neural network cheaply on synthetic data and transfer knowledge to recognize words in real data. Third, we set up experiments to compare model performances when using Canny edge detection and Chu's 3D thinning algorithm as preprocessing methods. We found that Canny edge detection performs better in most cases.

关键词： Feature extraction Image edge detection Training Neural networks Deep learning Three-dimensional displays

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Patent Analytics of Robotics Technology for intelligent Manufacturing in the Semiconductor Industry

Patent Analytics of Robotics Technology for Intelligent Manu...

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international conference on Computer Supported Cooperative Work in Design

作者： Paul P.J. Chen Amy I.C. Trappey Betty H.L. Lin Charles V. Trappey Department of Industrial Engineering and Engineering Management National Tsing Hua University Taiwan Department of Management Science National Chiao Tung University Taiwan

ISBN: (纸本)9781538614839

Manufacturers are making a transition to intelligent production, particularly in high-tech industries. Robotic arms play a crucial role in the effectiveness of transforming processes. This research searches and analyzes patents related to robotic arms utilized by intelligent manufacturing systems. For this preliminary study, global patent trends are discussed which help evaluate the technology domain to be developed. Key words from a literature review are used to search for relevant patents, and then the patent text is analyzed using text mining software and program. The core technique applied to the patent document analysis is “Term Frequency-Inverse Document Frequency” (TF-IDF). Using TF-IDF, the patent documents are grouped into meaningful sub-technology cluster. Thus, the economic data related to the semiconductor industry is analyzed to correlate the economic impact which may be related to the transitions in technology. The data of Taiwan firms were targeted for anticipating research directions and expand intellectual property domains to invest in and pursue. For the advanced analysis, the international patent codes for global and local companies are compared. The final results help define the strength and weakness of Taiwanese robotic technology development for the semiconductor industry. Patent cluster and patent distribution analysis via a technology function matrix are the primary research approaches used for the case study. The research prepares for the development of a theoretical foundation to predict intelligent manufacturing trends.

关键词： Patents Service robots Manipulators Market research Manufacturing Industries

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An interaction-enhanced feature selection algorithm 22nd

An interaction-enhanced feature selection algorithm

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22nd Pacific-Asia conference on Advances in Knowledge Discovery and data Mining, PAKDD 2018

作者： Tang, Xiaochuan Dai, Yuanshun Xiang, Yanping Luo, Liang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783319930398

Feature selection is a crucial pre-processing step in machine learning and data mining. A popular approach is based on information theoretic measures. Most of the existing methods used low-dimensional mutual information terms that are ineffective in detecting high-order feature interactions. To fill this gap, we employ higher-order interactions for feature selection. We first relax the assumptions of MI-based methods to allow for higher-order interactions. A direct calculation of the interaction terms is computationally expensive. We use four-dimensional joint mutual information, a computationally efficient measure, to estimate the interaction terms. We also use the ‘maximum of the minimum’ nonlinear approach to avoid the overestimation of feature significance. Finally, we arrive at an effective feature selection method that makes use of higher-order interactions. To evaluate the performance of the proposed method, we compare it with seven representative feature selection methods, including RelaxMRMR, JMIM, IWFS, CIFE, MIFS, MIM, and reliefF. Experimental results on eighteen benchmark data sets demonstrate that higher-order interactions are effective in improving MI-based feature selection. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Feature Selection

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Deep Sequential Recommendation for Personalized Adaptive User Interfaces 17

Deep Sequential Recommendation for Personalized Adaptive Use...

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22nd international conference on intelligent User Interfaces (IUI)

作者： Soh, Harold Sanner, Scott White, Madeleine Jamieson, Greg Univ Toronto Toronto ON Canada

ISBN: (纸本)9781450343480

Adaptive user-interfaces (AUIs) can enhance the usability of complex software by providing real-time contextual adaptation and assistance. Ideally, AUIs should be personalized and versatile, i.e., able to adapt to each user who may perform a variety of complex tasks. But this is difficult to achieve with many interaction elements when data-per-user is sparse. In this paper, we propose an architecture for personalized AULs that leverages upon developments in (1) deep learning, particularly gated recurrent units, to efficiently learn user interaction patterns, (2) collaborative filtering techniques that enable sharing of data among users, and (3) fast approximate nearest-neighbor methods in Euclidean spaces for quick UI control and/or content recommendations. Specifically, interaction histories are embedded in a learned space along with users and interaction elements;this allows the AUI to query and recommend likely next actions based on similar usage patterns across the user base. In a comparative evaluation on user-interface, web-browsing and e-learning datasets, the deep recurrent neural-network (DRNN) outperforms state-of-the-art tensor-factorization and metric embedding methods.

关键词： Adaptive User Interface Deep learning Personalization

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