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Detecting Depression in Social Media Posts Using machine learning 2nd

Detecting Depression in Social Media Posts Using Machine Lea...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Biradar, Abhilash Totad, S.G. Department of Computer Science B.V.B College of Engineering Hubli580031 India

ISBN: (纸本)9789811391866

The utilization of Social Networking Sites (SNS) like Twitter is expanding quickly and particularly by the more youthful age. The profit capacity of SNS enables us to express their interests, emotions and offer their day by day schedule. SNS sites such as Twitter allow for constant investigation of user behaviour. Such examples are important for the psychological research network to comprehend the periods and area of most prominent interest. Worlds fourth biggest disease depression has turned out to be a standout amongst the most huge research subject. We propose a system which uses tweets as source of data and SentiStrength sentiment analysis to create a training data for our system and a Back Propagation Neural Network (BPNN) model to classify the given tweets into depressed or not depressed categories. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Algorithms for Automatic Identification and Analysis of Sri Lankan Anopheles Mosquito Species

Algorithms for Automatic Identification and Analysis of Sri ...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Vigneshwaran Palanisamy Vaikunthavasan Thiruchenthooran Sinnathamby Noble Surendran Nagulan Ratnarajah Vavuniya Campus of the University of Jaffna Sri Lanka University of Jaffna Sri Lanka Faculty of Applied Science Vavuniya Campus of the University of Jaffna Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

Microscopic digital image processing algorithms are presented here to automatically detect primary morphological features of Sri Lankan anopheline mosquitoes, as an essential step towards the development of automated identification and analysis of various species of anopheline mosquitoes. Mosquitoes that belong to genus Anopheles spread the causative pathogen of malaria. Perfect and speedy species identification is crucial in any surveillance and control strategies. Currently, morphological taxonomic keys are used to identify various species. Two or more primary morphological characteristics, such as a number of dark spots of wings and pale bands of legs, are used in each step of the hierarchical key. To achieve the automatic detection of the primary morphological features, image processing algorithms performed at three levels. At the pre-processing level, methods work with raw, possibly noisy pixel values, with noise reduction and smoothing. In the mid-level, algorithms are utilized pre-processing results for further means with background removing and spots/bands segmentation. At the final level, techniques try to extract the semantics of spots/bands and counting the spots/bands from the information provided. Thirty samples of anopheline mosquitoes' wings and legs microscopic images were analysed with satisfactory results.

关键词： Legged locomotion machine learning algorithms Microscopy Veins Surveillance Feature extraction Data mining

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RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting 1

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10th international Workshop on machine learning in Medical Imaging (MLMI) / 22nd international conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Hoppe, Elisabeth Thamm, Florian Koerzdoerfer, Gregor Syben, Christopher Schirrmacher, Franziska Nittka, Mathias Pfeuffer, Josef Meyer, Heiko Maier, Andreas Friedrich Alexander Univ Erlangen Nurnberg Dept Comp Sci Pattern Recognit Lab Erlangen Germany Siemens Healthcare GmbH MR Applicat Dev Erlangen Germany

ISBN: (数字)9783030322489

ISBN: (纸本)9783030322489;9783030322472

Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T-1 and T-2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network. In this work, we propose a Recurrent Neural Network (RNN) architecture in combination with a novel quantile layer. RNNs are well suited for the processing of time-dependent signals and the quantile layer helps to overcome the noisy outliers by considering the spatial neighbors of the signal. We evaluate our approach using in-vivo data from multiple brain slices and several volunteers, running various experiments. We show that the RNN approach with small patches of complex-valued input signals in combination with a quantile layer outperforms other architectures, e.g. previously proposed Convolutional Neural Networks for the MRF reconstruction reducing the error in T-1 and T-2 by more than 80%.

关键词： Deep learning Recurrent Neural Networks Magnetic resonance fingerprinting reconstruction

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IOP conference Series: Materials Science and Engineering

IOP Conference Series: Materials Science and Engineering

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2019 2nd international conference on Advanced Electronic Materials, Computers and Materials Engineering, AEMCME 2019

The proceedings contain 93 papers. The topics discussed include: integration of building information model and RFID tag;construction safety education system based on virtual reality;emotional analysis system of book review based on neural network;three-dimensional landscape modeling of Quancheng Square in Jinan city;design and implementation of universal acoustic signal processing platform;an automatic text summary extraction method based on improved textrank and TF-IDF;abrupt motion tracking of ochotona curzoniae via improved motion model based on particle filter;computer simulation of crossing line based geometric illusions;face recognition based on joint sparse representation of multiple features for public safety;and SAR target recognition based on modified sparse representation for ground safety.

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Multichannel Electroencephalography-based Emotion Recognition Using machine learning 2

Multichannel Electroencephalography-based Emotion Recognitio...

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2nd international conference on Mechanical, Electronics, Computer, and Industrial Technology, MECnIT 2018

作者： Yulita, I.N. Julviar, R.R. Triwahyuni, A. Widiastuti, T. Departement of Computer Science Universitas Padjadjaran Jl. Raya Bandung - Sumedang Km. 21 Sumedang45363 Indonesia Faculty of Psychology Universitas Padjadjaran Jl. Raya Bandung - Sumedang Km. 21 Sumedang45363 Indonesia

In recent years, research in the field of human-computer interaction (HCI) has focused on strengthening machine functions in recognizing and understanding human emotions. Emotion recognition can be done in several ways, among others, through sounds, facial expressions, or a combination of both. The different sounds and facial expressions from different races and nations cause less accurate in the reading of emotions using these methods. Another method for recognizing emotions can be done by analysing the data from an electroencephalograph (EEG). The EEG signals from the human brain are the result of various activities carried out. One of them is emotion. The EEG signal used in this study came from the DEAP dataset. This dataset consists of 32 files, each of which contains 40 EEG recordings. The emotions from this dataset are classified based on the dimensions of arousal and valence. The signal was then decomposed into three different frequency groups (alpha, beta, and gamma) through band-pass filtering. After that, the principal component analysis (PCA) and resampling were carried out. The classification processes used a number of methods of machine learning. The result was known that the performance of K-star was the highest while naïve Bayes was the lowest. The accuracies of K-star in arousal and valence classification were 81.2, 82.6, respectively. The naive Bayes got 51.2 for the arousal, and 52.5 for the valence. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Emotion Recognition

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Reinforcement learning Based UAV Trajectory and Power Control Against Jamming 2nd

Reinforcement Learning Based UAV Trajectory and Power Contro...

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2nd international conference on machine learning for Cyber Security, ML4CS 2019

作者： Lin, Zihan Lu, Xiaozhen Dai, Canhuang Sheng, Geyi Xiao, Liang Department of Communication Engineering Xiamen University XiamenFujian China National Mobile Communications Research Laboratory Southeast University NanjingJiangsu China

ISBN: (纸本)9783030306182

Unmanned aerial vehicles (UAVs) are vulnerable to jamming attacks that aim to interrupt the communications between the UAVs and ground nodes and to prevent the UAVs from completing their sensing duties. In this paper, we design a reinforcement learning based UAV trajectory and power control scheme against jamming attacks without knowing the ground node and jammer locations, the UAV channel model and jamming model. By evaluating the UAV transmission quality obtained from the feedback channel and the UAV channel condition, this scheme uses reinforcement learning to choose the UAV trajectory and transmit power based on the UAV location, signal-to-interference-and-noise ratio of the previous sensing data signal received by the ground node, and the radio channel state. Simulation results show that this scheme improves the quality of service of the UAV sensing duty given the required UAV waypoints and saves the UAV energy consumption. © 2019, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots 2nd

Shot-Net: A Convolutional Neural Network for Classifying Dif...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Foysal, Md. Ferdouse Ahmed Islam, Mohammad Shakirul Karim, Asif Neehal, Nafis Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh College of Engineering and IT Charles Darwin University Darwin Australia

ISBN: (纸本)9789811391804

Artificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different machine learning and Computer Vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as "Shot-Net" in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Data Analytics

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The Vending Shopper Science Lab: Deep learning for Consumer Research 2nd

The Vending Shopper Science Lab: Deep Learning for Consumer ...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on Pattern Recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on Image Analysis and processing, ICIAP 2019

作者： Allegrino, Fioravante Gabellini, Patrizia Di Bello, Luigi Contigiani, Marco Placidi, Valerio Sogeda S.R.L. Via Volturno 10 San Giovanni TeatinoChieti66020 Italy Grottini Lab S.R.L. Via Santa Maria in Potenza Porto Recanati62017 Italy

ISBN: (纸本)9783030307530

To understand human behavior, a fundamental aspect is the analysis of the face and movement. This aspect is particularly important in the context of sales, where to know the shopper also means to guide purchases. A major challenge for vending environment is to predict the shopper behavior, with the aim to influence and increase purchases. In this ambit, vending machine industry is actually an interesting and growing data-driven marketing area of research. In this context, the aim of this paper is to propose an innovative architecture that is able to integrate face and movement understanding in a common strategy for real time consumer modeling. The vending machine and the decision support system process multimedia data to smartly respond with dynamic pricing and product proposal to the particular shopper which is in front of a vending machine. The aim is to build an intelligent vending machine which in real time is able to suitably propose products to a labelled shopper. The results come from real environments vending lab with 30 locations and about 1 million consumers in Italy, and have the aim to demonstrate the good performances and high efficiency of our solution in recognizing the age and the gender of consumer and different interactions with the vending machine. © 2019, Springer Nature Switzerland AG.

关键词： Deep learning

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Open domain chatbot based on attentive end-to-end Seq2Seq mechanism 2

Open domain chatbot based on attentive end-to-end Seq2Seq me...

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2nd international conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

作者： Abdullahi, Saheed Salahudeen Yiming, Sun Abdullahi, Abdulkadir Aliyu, Umar Computer Science and Technology Changchun University of Science and Technology Changchun Jilin China

ISBN: (纸本)9781450372619

Chatbot as a conversational system that can interact with human naturally is a Natural Language processing task that require modeling semantics of complicated relationships of the language for communication. Various attempts have been made to reduce the complexities (language understanding, feature extraction, domain recognition, intent detection, semantic slot filling and language generation) of training text-based Chatbot. While Traditional machine learning models are usually unable to be truly generic, recent advances in deep learning allow end-to-end models to be trained with large dataset. We train an open domain Chatbot end-to-end by directly mapping input tags as sequences to generate optimized output sequence tags. Our model is trained on Google Tensorflow framework running GPUs with deep neural architecture of sequence-to-sequence and attention mechanism. The interaction of the chatbot with human performed common sense reasoning. It also achieved a competitive result when quantified based on Human Performance Evaluation. However, we suggest networked reinforcement learning to incrementally update the consistency drawbacks towards achieving a generic Chatbot. © 2019 ACM.

关键词： Large dataset

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Adiabatic air water 2-phase flow in circular micro-channel using heterogeneous particle swarm optimization 2nd

Adiabatic air water 2-phase flow in circular micro-channel u...

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2nd international conference on Communication and Computing systems, ICCCS 2018

作者： Kumar, Sanjeev Shrivastava, Ananta Sahu, Poshan Lal Department of Mechanical Engineering Dronacharya College of Engineering GurgaonHaryana India

ISBN: (纸本)9780367001476

The goal of optimization is finding the minimum value for the target function, determining the initial values for algorithm parameters is important. In case that the initial values are not chosen rightly, the algorithm may diverge or may converge to a suboptimal solution. Important parameters in optimization algorithm include min and max values of speed, min and max values of position, and learning parameters c1 and c2. The particle that is closer to the target has more competence. First a group of particles are created randomly, and by updating groups, one of them may seek to optimize the solution. The best position obtained so far is called best. The other best value used by the algorithm is called best which is the best position obtained so far by the population. In some literatures the particle only chooses other particles which are its neighbors topologically;in such situation the best local solution is called best. If enough particles are given, PSO guarantees to get the global optimum solution of the objective function. A gas quality of very from 0.90 to 0.94 was obtained by using initial velocity state of particle swarm technique, by using tow factorized flow arrangements occurred as of time for the heterogeneous and homogenous network processing micro channel kept under fixed inlet manifold conditions and the single micro-channels was reassessed with two-phase parallel flows. To prevent Mixing of different parts, Wrong parts or a Parts box of less than unit quantity, set the handling rules of partial parts boxes, having been generated unavoidably, and have operators obey the rule. © 2019 Taylor & Francis Group, London.

关键词： Parallel flow

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