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Layout Classification of Red Onion Disease on Onion Leaf image Using Artificial Neural Network 1

Layout Classification of Red Onion Disease on Onion Leaf Ima...

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1st Paris Van Java international Seminar on Computer, Science, Engineering, and Technology, PVJ ISComSET 2020

作者： Mubarokhah, U.N. Dijaya, R. Maulana, M.I. Computer Science Universitas Muhammadiyah Sidoarjo Sidoarjo Indonesia

Shallots are one of the main ingredients that are often found throughout Indonesia. The optimal yield of onion plants is influenced by several factors, one of which is disease. The purpose of this study is to classify the image of leeks to identify onion diseases using Artificial Neural Network (ANN). Disease classification on leeks is leaf rot (anthracnose) and purple spots. The steps taken in the classification of leek disease are data input, pre-processing, feature extraction, machine learning. Several steps are carried out on the leek image, among others, improving image contrast, conversion of sRGB to LAB, segmentation using clustering, grayscale images, and binary images. After pre-processing, the next step is extracting features based on colour features and texture features. The colour features consist of standard Deviation, Kurtosis, Mean, and Skewness. While the texture features consist of Contrast, Correlation, Energy, Entropy, Variance, and IDM. The end result of this system will show whether the leeks are included in the class of leaf rot (anthracnose) or purple spots or healthy leaves. © Published under licence by IOP Publishing Ltd.

关键词： Binary images

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1st international conference on Systems and Information Sciences, ICCIS 2020

1st International Conference on Systems and Information Scie...

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1st international conference on Systems and Information Sciences, ICCIS 2020

ISBN: (纸本)9783030591939

The proceedings contain 37 papers. The special focus in this conference is on Systems and Information Sciences. The topics include: Development of an Algorithm Capable of Classifying the starting, Gear Change and Engine Brake Variables of a Vehicle by Analyzing OBD II Signals;Characterization of Braking and Clutching Events of a Vehicle Through OBD II Signals;PEstEL Analysis as a Baseline to Support Decision-Making in the Local Textile Industry;occupational Health and Safety for Decision-Making in the Framework of Corporate Social Responsibility: Models, Guidelines, and Indicators;model of Classification of University Services Using the Polarity of Opinions Taken from Social Networks;clustering Analysis of Electricity Consumption of Municipalities in the Province of Pichincha-Ecuador Using the K-Means Algorithm;Behavioral Signal processing with machine learning Based on FPGA;complementary Admission Processes Implemented by Ecuadorian Public Universities Promote Equal Opportunities in Access: An Analysis Through Knowledge Discovery in Databases;LOLY 1.0: A Proposed Human-Robot-Game Platform Architecture for the Engagement of Children with Autism in the learning Process;aphids Detection on Lemons Leaf image Using Convolutional Neural Networks;alternative Psychological Treatment for Patients with a Diagnosis of Entomophobia with Virtual Reality Environments;designing a Lighting System for Smart Homes;green Energy for the Reception and processing of Satellite and Microwave Signals;Network Design Defined by Software on a Hyper-converged Infrastructure. Case study: Northern Technical University FICA Data Center;study of the Fronthaul WDM Applied to the Cloud RAN of 5G-CRAN;Evaluation of the Broadcast storm Problem Based on Hybrid Retransmissions Algorithms in FANET Networks.

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Does the learning principle help us improve hand-crafted finger-knuckle-print features? 1

Does the learning principle help us improve hand-crafted fin...

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1st international conference on Communications, Control Systems and Signal processing, CCSSP 2020

作者： Haouam, Mohamed Yassine Meraoumia, Abdallah Bendib, Issam Laimeche, Lakhdar Algeria

ISBN: (纸本)9781728158358

One of the most critical steps in pattern recognition systems is to extract the most distinctive features of the image. Therefore, the accuracy of these systems is strongly related to the efficiency of this step to represent the image. In general, hand-craft image features are obtained using feature extraction methods that often have no prior knowledge about the image type, which may result in poor features. In this paper, a hand-craft image feature learning method, based on Local Binary pattern (LBP), for Finger-Knuckle-Print representation and recognition is applied. Thus, in the learning phase, each training finger image is first divided into several non-overlapping regions, and the feature filter and the codebook of each region are individually learned. For the enrolment phase, the obtained learned filter and codebook are applied to each image to extract the histogram feature of each region. Then, the different histograms are concatenated in a single feature vector to represent the finger features. Finally, an SVM classifier is used for the identification phase. The experimental results clearly show that the learning principle offers a good gain in the system accuracy, which can provide an excellent identification rate and thus improve security. © 2020 IEEE.

关键词： Feature extraction

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Properties of Unique Degree Sequences of 3-Uniform Hypergraphs 1st

Properties of Unique Degree Sequences of 3-Uniform Hypergrap...

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1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Ascolese, Michela Frosini, Andrea Kocay, William Lawrence Tarsissi, Lama Universitadi Firenze Dipartimento Sistemi & Informat Florence Italy Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Manitoba St Pauls Coll Winnipeg MB Canada Univ Gustave Eiffel ESIEE CNRS LIGM F-77454 Paris France Sorbonne Univ Abu Dhabi Dept Sci & Engn Abu Dhabi U Arab Emirates

ISBN: (纸本)9783030766566;9783030766573

In 2018 Deza et al. proved the NP-completeness of deciding wether there exists a 3-uniform hypergraph compatible with a given degree sequence. A well known result of Erdos and Gallai (1960) shows that the same problem related to graphs can be solved in polynomial time. So, it becomes relevant to detect classes of uniform hypergraphs that are reconstructible in polynomial time. In particular, our study concerns 3-uniform hypergraphs that are defined in the NP-completeness proof of Deza et al. Those hypergraphs are constructed starting from a non-increasing sequence s of integers and have very interesting properties. In particular, they are unique, i.e., there do not exist two non isomorphic 3-uniform hypergraphs having the same degree sequence d(s). This property makes us conjecture that the reconstruction of these hypergraphs from their degree sequences can be done in polynomial time. So, we first generalize the computation of the ds degree sequences by Deza et al., and we show their uniqueness. We proceed by defining the equivalence classes of the integer sequences determining the same ds and we define a (minimal) representative. Then, we find the asymptotic growth rate of the maximal element of the representatives in terms of the length of the sequence, with the aim of generating and then reconstructing them. Finally, we show an example of a unique 3-uniform hypergraph similar to those defined by Deza et al. that does not admit a generating integer sequence s. The existence of this hypergraph makes us conjecture an extended generating algorithm for the sequences of Deza et al. to include a much wider class of unique 3-uniform hypergraphs. Further studies could also include strategies for the identification and reconstruction of those new sequences and hypergraphs.

关键词： Hypergraph Graphic sequence Uniqueness problem Analysis of algorithms

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machine learning Methods for Extraction and Classification for Biometric Authentication 1st

Machine Learning Methods for Extraction and Classification f...

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1st international conference on Data Science, machine learning and Applications, 2019

作者： Gunjan, Vinit Kumar Prasad, Puja S. Pathak, Rashmi Kumar, Amit Department of CSE CMR Institute of Technology Hyderabad India Department of CSE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Siddhant College of Engineering Pune India Department of CSE SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789811514197

In this paper our main focus is to discover different machine learning techniques that are useful biometric System. As biometric authentication system is a combination of both image processing and pattern recognition, in this classification of pattern is a difficult task. machine learning have number of algorithm that makes classification task easy. machine learning is divided as supervised as well as unsupervised learning. In unsupervised learning the machine construct representation of input by getting inputs x1, x2, x3, …, and this is used for decision making, predicting future inputs or we say unsupervised learning finds patterns in the data and mainly solve clustering problem. In supervised learning set of output is already given, only we have to find this set of output from respective input value. In this paper we also discuss the area of machine learning where already work has done for biometric. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Unsupervised learning

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Video captioning for proactive video management using deep machine learning 1st

Video captioning for proactive video management using deep m...

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1st international conference on Frontiers in Smart Systems Technologies, ICFSst 2019

作者： Om Prakash, S. Udhayakumar, S. Anjum Khan, R. Priyadarshan, R. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9789811550287

Inspired by the new advancements in machine learning and search engine technology, it is possible to caption videos and index them for efficient managing in applications like YouTube, Dailymotion, Netflix, etc. This system can generate a set of tags which when put together will give a sensible translation of the video in text format. The key challenge is the semantic gap between identification of objects by the human brain and the computer AI. In this proposed model, we propose to transcribe the video clips based on the contents and construct a pipeline through the convolutional neural network (CNN) which will efficiently and accurately process the video and search for the contents. This would require processing of the video clip and indexing it in a database. The same indexing will enable the use of this technology in content-based image retrieval (CBIR) tasks. The user can search for videos based on the context, and the time spent is seeking through the video to search for the scene is solved. We use deep learning model to search through video content and show that it performs efficiently. © Springer Nature Singapore Pte Ltd 2021.

关键词： Video signal processing

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1st international conference on Emerging Global Trends in Engineering and Technology, EGTET 2020

1st International Conference on Emerging Global Trends in En...

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1st international conference on Emerging Global Trends in Engineering and Technology, EGTET 2020

ISBN: (纸本)9789811615498

The proceedings contain 21 papers. The special focus in this conference is on Emerging Global Trends in Engineering and Technology. The topics include: Flat Plate Solar Thermal Collectors—A Review;a Smart Feature Reduction Approach to Detect Botnet Attack in IoT;an Approach to Handle Heterogeneous Healthcare IoT Data Using Deep Convolutional Neural Network;designing of NimbleArm—A Low-Cost, Semi-autonomous Interactive Robotic Arm;Smart Refri: SMART REFRIGERATOR for Tracking Human Usage and Prompting Based on Behavioral Consumption;cascade-Based Pedestrian Detector Using Edge and pattern Features;nanomaterial-Based Microstrip Patch Antenna Array for X Band Operation;study of TiO2 and ZnO Nanostructures as Wide Band Gap Semiconductor for the Application of Nanotechnology-Based Solar Cells;preface;integration of IoT and Blockchain Technology for Smart Cities;AGC of Hybrid Solar-Hydro-Thermal System with GWO-based Conventional Secondary Controllers;study on Substrate Dependency of Graphene-Based Patch Antennas for Gigahertz and Terahertz Applications;novel Approach to Reduce Rate of False Fire Alarms Through Multivariate Characterization of Fire;a Graph-Based Band Selection Method for Hyperspectral images Using Correlation Matrix;a study on Using Deep learning for Segmentation of Medical image;Attention recognition System in Online learning Platform Using EEG Signals;Comparative study of Jute Fiber and PET Fiber-Reinforced Concrete;reused-Based Replacement Policy for Last-Level Cache with Minimum Hardware Cost;revealing structural and Optoelectronic Properties for Bi-Doped CuGaS2 Chalcopyrite: A Density Functional Investigation.

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An image processing Based American Sign Language Fingerspelling Interpreter 1

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1st international Virtual conference on Industry 4.0, IVCI4.0 2020

作者： Sirisha, Gopireddy Harshdeepsinh, Parmar Konduri, Hemanth Narayanan, Sathiya School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (数字)9789811612442

ISBN: (纸本)9789811612435

Owing to limitations such as high cost, discomfort, and connectivity issues, existing fingerspelling interpreters fail to present a feasible and comfortable medium to efficiently communicate with communities that rely on sign language that includes hand gestures, facial expressions and orientations, for communication. Biological measurements, based on aspects such as hand or arm movements and acoustic variations at wrist, are unique to an individual and require customized systems to be designed for many groups of people. However, developing a general system that would not only incorporate sign language translation, but also additional features would bring a revolutionary change in differently abled community. In this article, we propose an image processing based system with machine learning algorithms, implemented as a WebApp on Raspberry Pi, which is an efficient, cost-effective, and user-friendly bridge between communities that understand sign language and the communities that do not. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feedforward neural networks

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Read Textual features in images and convert to Editable form by extended use of Artificial Neural Networks, Deep learning and Maximally stable Extremal Region techniques 1

Read Textual features in Images and convert to Editable form...

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1st international conference on Advances in Smart Sensor, Signal processing and Communication Technology, ICASSCT 2021

作者： Joseph Gladwin, S. Vinoth Kumar, C. Department of Electronics of Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Nowadays most of the information is stored in images and there is a need to convert this information into an editable format. The objective of this paper includes the development of a user-friendly tool to extract text from the video or to recognize well-written handwriting in a scanned document to an editable form. The proposed methodology is robust and can provide high-grade performance with layout distortion. The explication utilizes modalities like Optical Character recognition (OCR), Artificial Neural Networks (ANN), Deep learning and Maximally stable Extremal Regions (MSER) techniques for text detection. The video extraction can be attained by using video to frame conversion and then to use the structural Similarity Index Measure (SSIM). The prototype is developed using MATLAB and provided with a GUI, which is deployable on a workstation. The generated GUI is employed to define Region of Interest (ROI), specify the required text layout, highlighting the specific portions of the image, select the appropriate language and also export the text to a word document or notepad applications where it can be edited. © Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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A pattern recognition based Holographic Graph Neuron for Persian alphabet recognition 1

A pattern recognition based Holographic Graph Neuron for Per...

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1st international conference on machine Vision and image processing, MVIP 2020

作者： Hajihashemi, Vahid Arab Ameri, Mohammad Mehdi Alavi Gharahbagh, Abdoreza Bastanfard, Azam Kharazmi University Faculty of Engineering Tehran Iran Islamic Azad University Shahrood Branch Faculty of Engineering Shahrood Iran

ISBN: (纸本)9781728168326

In this article a Vector Symbolic Architectures is purposed to implement a hierarchical Graph Neuron for memorizing patterns of Persian/Arabic isolated characters. The main challenge in this topic is using Vector Symbolic representation as a one-layered design for neural network while maintaining the previously reported properties and performance characteristics of hierarchical Graph Neuron. The designed architecture is robust to noise and enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern. The proposed method was implemented on a standard Persian database and the obtained results showed the ability of (not necessarily better) Graph neuron to recognize the Persian isolated character patterns. © 2020 IEEE.

关键词： Holography

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