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Ekush: A Multipurpose and Multitype Comprehensive Database for Online Off-Line Bangla Handwritten Characters 2nd

Ekush: A Multipurpose and Multitype Comprehensive Database f...

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

作者： Rabby, A K M Shahariar Azad Haque, Sadeka Islam, Md. Sanzidul Abujar, Sheikh Hossain, Sayed Akhter Department of Computer Science and Engineering Daffodil International University Dhanmondi Dhaka1205 Bangladesh

ISBN: (纸本)9789811391866

Ekush the largest dataset of handwritten Bangla characters for research on handwritten Bangla character recognition. In recent years machine learning and deep learning application-based researchers have achieved interest and one of the most significant application is handwritten recognition. Because it has the tremendous application such in Bangla OCR. Also, Bangla writing script is one of the most popular in the world. For that reason, we are introducing a multipurpose comprehensive dataset for Bangla Handwritten Characters. The proposed dataset contains Bangla modifiers, vowels, consonants, compound letters and numerical digits that consists of 367,018 isolated handwritten characters written by 3086 unique writers which were collected within Bangladesh. This dataset can be used for other problems i.e.: gender, age, district base handwritten related research, because the samples were collected include verity of the district, age group and the equal number of male and female. It is intended to fabricate acknowledgment technique for hadn written Bangla characters. This dataset is unreservedly accessible for any sort of scholarly research work. The Ekush dataset is trained and validated with EkushNet and indicated attractive acknowledgment precision 97.73% for Ekush dataset, which is up until this point, the best exactness for Bangla character acknowledgment. The Ekush dataset and relevant code can be found at this link: https://***/ShahariarRabby/ekush. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Deep learning for 3D Classification Based on Point Cloud with Local Structure

Deep Learning for 3D Classification Based on Point Cloud wit...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yanan Song Xinyu Li Liang Gao State Key Lab. of Digital Manufacturing Equipment & Technology Huazhong University of Science & Technology Wuhan P. R. China

Point cloud consists of many unordered and unstructured points, which makes the simple deep learning (DL) network hard to capture the local structure of point cloud. This shortcoming limits the ability of the DL network to recognize the fine-grained features of objects. Network structure is changed in some studies for this problem, but this increases the network complexity. This paper proposes an effective preprocessing method for point cloud to deal with this problem. The local region that represents the local structure of point is searched by using a cube with fixed side length. All of the points in the local region are used to construct the feature vector of the center point located at the center of the cube. These feature vectors are input into a simple convolutional neural network. The ModelNet40 shape classification benchmark is used to evaluate the proposed method. Experimental results show that the proposed method improves the classification accuracy of the simple deep learning network.

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Automatic Interval Selection on LA-ICPMS Isotopic Profiles Using an Extreme learning machine (ELM) Network 22

Automatic Interval Selection on LA-ICPMS Isotopic Profiles U...

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22nd international conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Marti-Puig, Pere Perez-Mayol, Silvia Serra-Serra, Moises Palmer, Miguel Univ Vic Cent Univ Catalonia U Sci Tech Data & Signal Proc Grp C Laura 13 Vic 08500 Spain IMEDEA Inst Mediterrani Estudis Avancats CSIC UIB C Miquel Marques 21 Illes Balears 07190 Spain

ISBN: (纸本)9781643680156;9781643680149

Laser Ablation - Inductively Coupled Plasma Mass Spectrometry (LAICPMS) is a surface-based technique used to quantify the chemical composition of a solid to its elemental and isotopic level. The output signal for each LA-shot corresponds to a set of time series, in intensities (counts-per-second, cps), that provides information on the quantity of each isotope. LA-ICPMS is widely used in biological sciences. For instance in fish ecology, it is used to analyze fish otoliths (ear stones) to obtain information on the fish's life history (i.e., origin, migrations or exposure to contaminants). The experimental protocol for translating the actual output from LA-ICPMS into isotope concentration is long and complex. The first step is specially time consuming: the intensities obtained from each shot have to be reviewed one by one by an expert to eliminate procedural spikes and define the intervals that optimally represent (1) the background noise (blank) and (2) the background noise plus the signal (plateau). Here we propose a method to facilitate this first step using a trained neural network. The ELM was trained using cases previously processed to emulate the decisions of the expert. Our results showed that in comparison to the manual treatment the quality of the assessment with ELM was optimal for an automatic processing.

关键词： Extreme learning machines (ELM) LA-ICPMS Fish otoliths

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Electromagnetic Spectrum Threat Prediction via Deep learning 2nd

Electromagnetic Spectrum Threat Prediction via Deep Learning

引用

2nd EAI international conference on Advanced Hybrid Information processing, ADHIP 2018

作者： Wei, Chunyan Qi, Lin Wu, Ruowu Lin, Yun College of Information and Communication Engineering Harbin Engineering University Harbin150001 China LuoyangHenan471003 China

ISBN: (纸本)9783030190859

Nowadays, in the complex electromagnetic environment, the detection of foreign satellite, the electronic interferences and the sensing data tampering in the process of consistent spectrum situation fusion and the electronic countermeasures reconnaissance and enforcement implemented by the enemy electronic attacks all pose serious threats to the communication performance of our electronic devices and communication systems. Therefore, how to detect these electromagnetic spectrum threats effectively is very important. The generative adversarial networks was applied in this paper, which is a method in deep learning, and an unsupervised solution for the above-mentioned electromagnetic spectrum threat signal prediction problem was provided, which has achieved good results. To carry out the detection experiments, three common electromagnetic spectrum threat scenarios were simulated. The prediction performance of the model is evaluated based on the prediction accuracy of the model. The experimental results have shown that the generative adversarial networks model used in this paper has a good predictive effect on the electromagnetic spectrum threat signals of a certain intensity. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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Message from conference Patron

Proceedings of 2nd International Conference on Innovations i...

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proceedings of 2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019 2019年 I页

作者： Biswal, Bibhuti Bhushan Patron India

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An assistive bot for healthcare using deep learning: Conversation-as-a-service 2nd

An assistive bot for healthcare using deep learning: Convers...

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2nd international conference on Advanced Computing and Intelligent Engineering, ICACIE 2017

作者： Shah, Dhvani Philip, Thekkekara Joel St. John College of Engineering and Management Palghar India Universal College of Engineering Vasai-Virar India

ISBN: (纸本)9789811317071

Gone are the days when software was used only for complex mathematical calculations or graphical motions alone. Today, it is software that has exponentially grown to become more powerful and more human—most obviously in applications such as ‘Chatbots.’ The year 2017 marks the Chatbot revolution in various industries like health, career, insurance, customer care support. Artificial intelligence (AI), which is the key player in enabling human-like behavior intelligently, is dramatically changing business. Chatbots, fueled by AI, are becoming a viable option for human–machine interaction. Deep learning algorithms have made it possible to build intelligent machine. In this research, we have developed a HealthBot using TensorFlow and Natural Language processing (NLP) techniques. There is no denying that efficient patient engagement is a key challenge for all healthcare organizations and any company that can unravel this challenge can effectively earn high returns of investments. Chatbots are one of the major overhauls that hospitals can easily provide more customized care for patients while cutting down on the waiting period. The proposed HealthBot lists the common symptoms;then, based on user’s health issue it gets deeper into the conversations predicting the health problem of the user. Such bots are needed for today’s fast-moving population where they have no time to keep a tab on their health. Neural network implementation adds more accuracy to the responses. The proposed Chatbot model is a retrieval-based bot and of closed domain. Finally, the HealthBot is deployed on the Flask, a Python web development framework. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Application programs

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2nd Computational Methods in Systems and Software, CoMeSySo 2018

2nd Computational Methods in Systems and Software, CoMeSySo ...

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2nd Computational Methods in Systems and Software, CoMeSySo 2018

ISBN: (纸本)9783030002107

The proceedings contain 64 papers. The special focus in this conference is on Methods in Systems and Software. The topics include: Influence analysis of selected factors in the function point work effort estimation;The performance evaluation for the efficiency of coastal regional innovation network based on DEA;models and algorithms of vector optimization in selecting security measures for higher education institution’s information learning environment;implementing DevOps in legacy systems;development of sectoral intellectualized expert systems and decision making support systems in cybersecurity;optimal multi-robot path finding algorithm based on A*;an agent-architecture for automated decision-making on the semantic web;An efficient hardware realization of DCT based color image mosaicing system on FPGA;semantic history: Ontology-based modeling of users’ web browsing behaviors for improved web page revisitation;a novel template - Based data structurization scheme for normalizing and analyzing medical data;a recovery technique for the fog-computing-based information and control systems;an examination of message ferry movement for delay tolerant networks;Comparison of ERP systems with blockchain platform;image content protection using hybrid approach of blocking and embedding algorithm in the context of social network;Calculation of robustly stabilizing PI controllers for linear time-invariant systems with multiplicative uncertainty;Data extraction from the distorted DCF77 signal captured using low-cost receivers;a mobile terrestrial surveillance robot using the wall-following technique and a derivative integrative proportional controller;BrainMRI enhancement as a pre-processing: An evaluation framework using optimal gamma, homographic and DWT based methods;efficiency comparison of modern computer languages: Sorting benchmark.

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2nd Computational Methods in Systems and Software, CoMeSySo 2018

2nd Computational Methods in Systems and Software, CoMeSySo ...

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2nd Computational Methods in Systems and Software, CoMeSySo 2018

ISBN: (纸本)9783030001834

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A Multi-task learning Approach Based on Convolutional Neural Network for Acoustic Scene Classification 19

A Multi-task Learning Approach Based on Convolutional Neural...

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proceedings of the 2019 2nd international conference on Algorithms, Computing and Artificial Intelligence

作者： Kuilong Xu Shilei Huang Gang Cheng Xiao Song School of Computer Science and Technology Anhui University Hefei China Shenzhen Raisound Technologies Co. Ltd Shenzhen China Shenzhen Raisound Technologies Co. Ltd Shenzhen China Peking University Shenzhen Institute Shenzhen China

ISBN: (纸本)9781450372619

Acoustic Scene Classification (ASC) aim to recognize an acoustic scene in audio signal records. The acoustic scene is a mixture of background sounds and various sound events, and sound events often determine the type of acoustic scene. However, in many research methods for acoustic scene classification, only a few people have noticed the important information of sound events. In this paper, we combine the ASC task and Sound Event Detection (SED) task, and propose a new CNN approach with multi-task learning (MTL), which uses SED as an auxiliary task to pay more attention to the information of the sound event in the model. Besides, in view of the characteristic of the sound event with high-energy time-frequency components, we use Global Max Pooling (GMP) instead of the Fully Connected layer (FC) in the traditional CNN. The advantage is that the model focused on distinct high-energy time-frequency components of audio signals (sound event). Finally, extensive experiments are carried out on the TUT acoustic scene 2017 dataset. Our proposed CNN approach with MTL shows better generalization, and improves the Unweighted Average Recall (UAR) of 5.2% over the DCASE 2017 ASC baseline system.

关键词： Multi-task learning Convolutional Neural Network Acoustic Scene Classification Sound Event Detection

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6th international conference on Future Data and Security Engineering, FDSE 2019

6th International Conference on Future Data and Security Eng...

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6th international conference on Future Data and Security Engineering, FDSE 2019

ISBN: (纸本)9783030356521

The proceedings contain 54 papers. The special focus in this conference is on Future Data and Security Engineering. The topics include: Computing History-Dependent Schedules for Processes with Temporal Constraints;finding All Minimal Maximum Subsequences in Parallel;OCL2PSQL: An OCL-to-SQL Code-Generator for Model-Driven Engineering;framework for Peer-to-Peer Data Sharing over Web Browsers;efficiently Semantic-Aware Pairwise Similarity: an Applicable Use-Case;lower Bound on Network Diameter for Distributed Function Computation;a Combined Enhancing and Feature Extraction Algorithm to Improve learning Accuracy for Gene Expression Classification;age and Gender Estimation of Asian Faces Using Deep Residual Network;Light-Weight Deep Convolutional Network-Based Approach for Recognizing Emotion on FPGA Platform;a New Test Suite Reduction Approach Based on Hypergraph Minimal Transversal Mining;metagenome-Based Disease Classification with Deep learning and Visualizations Based on Self-organizing Maps;On Analyzing the Trade-Off Between Over-Commitment Ratio and Quality of Service in NFV Datacenter;dynamic Data Management Strategy on Cloud Network by Fog Computing Model;openness in Fog Computing for the Internet of Things;A Top-Down Scheduling for Time Efficient Data Aggregation in WSNs;a New Technique to Improve the Security of Elliptic Curve Encryption and Signature Schemes;a Visual Model for Privacy Awareness and Understanding in Online Social Networks;A Method to Enhance the Security Capability of Python IDE;studying machine learning Techniques for Intrusion Detection Systems;enforcing Access Controls in IoT Networks;machine learning Based Monitoring of the Pneumatic Actuators’ Behavior Through signal processing Using Real-World Data Set;adventures in the Analysis of Access Control Policies.

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