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Unsupervised Image Generation and Manipulation Using Deep Convolutional Adversarial Networks 1

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2nd International Conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Ghadekar, Premanand Joshi, Shaunak Kokate, Yogini Kude, Harshada Department of Information Technology Vishwakarma Institute of Technology Pune India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

In recent years, there has been an outburst in the field of Computer Vision due to the introduction of Convolutional Neural Networks. However, Convolutional Neural Networks have been sparsely used for unsupervised learning. The advancement of computational power and large datasets provide large opportunities to apply deep learning for image processing. This paper proposes a new architecture based on Deep Convolutional Generative Adversarial Network (DCGAN) for unsupervised image generation, its usage for image manipulation tasks such as denoising, super-resolution, and deconvolution. This proposed model demonstrates that the learned features can be used for image processing tasks—demonstrating their applications for general use as DCGAN learns from large datasets and adds high-level image details and features where traditional methods cannot be used. While the image results from the proposed network architecture and the original DCGAN architecture are similar in terms of performance, they are visually better when viewed by humans. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Cardiovascular Signal Processing: State of the Art and Algorithms 1

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Future of Information and Communication Conference, FICC 2021

作者： Birhanu, Hiwot Kassaw, Amare Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia

ISBN: (数字)9783030731007

ISBN: (纸本)9783030730994

The emergence of Artificial Intelligence (AI) has brought many advancements in biomedical signal processing and analysis. It has opened the way for having efficient systems in the diagnosis and treatment of diseases such as Cardiovascular (CV) disorder. CV disorder is one of the critical health problems causing death to lots of peoples globally. Electrocardiogram (ECG) signal is the signal taken from the human body to diagnosis the status of CV and heart conditions. Earlier to the introduction of computers, the diagnosis of heart conditions was made by experts manually and that caused various mistakes. Currently, the usage of advancing signal processing devices help to reduce those errors and enables to develop effective signal detection and parameter estimation algorithms that are useful to analyze the parameters of ECG signals. Which intern supports to decide if the person is in critical condition and take an appropriate action. In this work, we analyze the performances of classical techniques and machine learning algorithms for ECG based CV parameters estimation. For this, first an in-depth review is done for both classical techniques and machine learning algorithms. Specifically, the benefits and challenges of machine learning and deep-learning algorithms for CV signal processing and parameter estimation is discussed. Then, we evaluate the performances of both classical (Kalman Filtering) and machine learning algorithms. The machine learning based algorithms are modeled with Butterworth low pass filter, wavelet transform and linear regression for parameter estimation. Besides, we propose an algorithm that combines adaptive Kalman filter (AKF) and discrete wavelet transform (DWT). In this algorithm, the ECG signal is filtered using AKF. Then, segmentation is performed and features are extracted by using DWT. Numerical simulation is done to validate the performances of these algorithms. The results show that at 20 % false positive rate, the detection performance o

关键词： Kalman filters

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Hybridized Metaheuristic Search Algorithm with Modified Initialization Scheme for Global Optimization 1

引用

2nd International Conference on Innovative Technology, Engineering and Sciences, iCITES 2020

作者： Choi, Zhi Chuan Ang, Koon Meng Lim, Wei Hong Tiang, Sew Sun Ang, Chun Kit Solihin, Mahmud Iwan Juhari, Mohd Rizon Mohamed Chow, Cher En Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur56000 Malaysia

ISBN: (数字)9783030709174

ISBN: (纸本)9783030709167

Metaheuristic search algorithms (MSAs) such as particle swarm optimization (PSO) and differential evolution (DE) have gained significant attentions from practitioners to solve different engineering applications. Nevertheless, these MSAs are only effective to solve certain problem categories due to No Free Lunch Theorem. A modified hybrid algorithm namely chaotic oppositional based hybridized DE with PSO (CO-HDEPSO) is proposed to address the aforementioned drawbacks in order to solve different global optimization problems more effectively. The chaotic oppositional based initialization scheme is first designed into CO-HDEPSO, aiming to produce initial population with better quality. A hybridization framework is introduced in CO-HDEPSO to combine the strengths of PSO and DE simultaneously for further optimization performance enhancement. Comprehensive analyses are performed to compare the performances of CO-HDEPSO with five MS algorithms using CEC 2014 benchmark functions. The proposed CO-HDEPSO is reported to demonstrate more competitive search accuracy than its peer algorithms in most benchmark functions and able to solve 26 out of 30 CEC 2014 test functions with best Fmean values. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Global optimization

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The Neural Network Based Decision Support System Using Aggregated Opinions of Public Observers 1

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11th World Conference on intelligent systems for Industrial Automation, WCIS 2020

作者： Aliyev, Elchin Rzayev, Ramin Abdullayev, Khanmurad Institute of Control Systems of ANAS Vahabzadeh str. 9 BakuAZ1141 Azerbaijan

ISBN: (数字)9783030680046

ISBN: (纸本)9783030680039

Creation of information technologies becomes especially actual when the accessible information is interpreted as a major and unique resource, a stock, a reserve, as an element of economic potential of the business entity, which should be used effectively for achievement of a competitive position in the market. Thereupon there is necessary a decision of some actual scientific problems: formation of the major concepts, development of theoretical bases of data resource management, creations of typical models of information systems for the enterprises, corporations and regional economy. In the framework of e-Governance the conception of decision making support system is offered on the base of the aggregated opinions of public observers. All estimations of these recipients are realized by fuzzy inference system and aggregated by structured neural network model. The neural network model combining two types three-layer feedforward neural networks, allows rather quickly reacting to possible changes in a conjuncture of recipients’ priorities. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural network models

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Smart Leaf Disease Detection Using Image Processing 1

引用

2nd International Conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Thakur, Amit Kulkarni, Nachiket K. Rajwal, Tanay Deshpande, Vivek Department of Electronics Engineering Vishwakarma Institute of Technology Pune India Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

The present agricultural scenario, farmers are suffering from one major problem, which is less crop productivity or crop losses, and farmers suffer a lot due to these losses. Crop losses are caused not just by weeds, but insect damage and plant diseases also contribute a significant amount to this cause. This paper gives some methods and efficient ways of detecting plant illness utilizing image processing and alarming the illness to the farmer by email, SMS and displaying the infection name on the framework users screen display. Also, we have introduced a distinguishing feature which is we do not only show the infection detected to the plant but also provide effective remedies to the farmer so that they can act fast on it. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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About One Feature Selection Algorithm in Pattern Recognition 1

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11th World Conference on intelligent systems for Industrial Automation, WCIS 2020

作者： Nishanov, A.H. Akbaraliev, B.B. Tajibaev, Sh. Kh. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent100095 Uzbekistan Karakalpak State University named after Berdakh Nukus100095 Uzbekistan

ISBN: (数字)9783030680046

ISBN: (纸本)9783030680039

In this paper researched one of the problems of pattern recognition, which plays an important role in the intellectual data analysis. One of the main problem in pattern recognition is to build mathematical model classify method and algorithm. Typically, a pre-processing of the data is performed using a pre-selection option before solving this problem. One of the issues of pre-processing is normalization, and the other is the adjustment of the spatial scale of the object under study. The article deals with the second issue, the transition from a large-sized space to a more complete small-sized space. To solve the problem, the Fisher function, which is well known in the processing of research objects, is selected, and based on this function, the Fisher’s type criteria are created. The Fisher’s type criterion is assumed to be a goal function, on the basis of which the optimization problem is formed. In the article, methods and algorithms for solving this optimization problem are developed, compared, analyzed and tested on model examples. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data handling

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Visual Analysis of Sales Temporal Evolution Using KMeans, MultiStream and GeoChart 1

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Future of Information and Communication Conference, FICC 2021

作者： Carlo, Corrales-Delgado Oscar, Ramirez-Valdez Enzo, Velasquez-Lobaton doctorandos en Ciencias de la Computación - UNSA Arequipa Peru

ISBN: (数字)9783030731038

ISBN: (纸本)9783030731021

In this study, we propose to employ KMeans, MultiStream and GeoChart to meet the complexity of information treatment in growing companies, and to visualize large amounts of data, in order to support timely decision-making with a Visual Analysis. KMeans allows information to be segregated by relating the gains to the distance between the nodes, MultiStream allows selecting a time segment and identifying patterns based on hierarchies of the selected segment and GeoChart allows a generic geographic visualization by adding the selected data in a data structure to visualize them on a map. Employing all three programs, KMeans, MultiStream and GeoChart, we analyze information from data banks of a transit company and illustrate how to arrive at a reliable cost/benefit analysis via a nine-step data analyzation procedure. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cost benefit analysis

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Gestalt Prototyping Framework - Evaluation Tool

Gestalt Prototyping Framework - Evaluation Tool

引用

4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Ripalda, Daniel Guevara, Cesar Garrido, Alejandra Univ Tecnol Indoamer Machala & Sabanilla Mechatron & Interact Syst Res Ctr Quito Ecuador Univ Nacl La Plata LIFIA Fac Informat RA-1900 La Plata Argentina CONICET 50 & 120 RA-1900 La Plata Argentina

ISBN: (纸本)9783030680176;9783030680169

The Gestalt Prototyping Framework linked the Nielsen Usability Heuristics to the principles of human perception and presents some parameters that can be used in developing high and low-fidelity prototypes for mobile application interfaces. The link between the fundamentals of usability and Gestalt principles focuses on the graphical components of the interfaces and the functions that they fulfill in the development of different functional actions. Previous articles have presented promising experimental results in reducing trial-error regressions in the interface design process and improving interface redesign processes;There have been positive results in parameters such as learnability, ease of use, perception of simplicity, and user preference. Within this same line of research, the present work describes the development process of an application that summarizes the fundamental aspects of the Gestalt prototyping framework in an evaluative model, which helps application designers, software engineers, and usability experts, to assess the prototypes of the interfaces and the incidence of the different graphic components in usability interactions. The tool is based on the most widely used usability parameters and is structured based on the responses that the development team fills in an automated LIKERT assessment. The results are processed based on an algorithm that simulates and predicts the results that could be obtained in high-fidelity prototype tests. This application issues specific recommendations on the visual components of the interfaces, to obtain better results in the production of high and low-fidelity prototypes.

关键词： Gestalt User interfaces Usability Mobile Agile software development

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Differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval 1

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3rd International Symposium on Signal and Image Processing, ISSIP 2020

作者： Dixit, Abhishek Mani, Ashish Bansal, Rohit Department of Computer Science Amity School of Engineering and Technology Amity University Noida U.P India Department of EEE Amity School of Engineering and Technology Amity University Noida U.P India Department of Management Studies Rajiv Gandhi Institute of Petroleum Technology Raebareli U.P. India

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

With the increase of multimedia devices on internet, enormous number of videos are being added as part of digital content. There is a huge challenge in retrieval of these videos as mostly the videos are kept in unstructured form. Intended users try to retrieve video content as per the relevancy and need. Shot boundary detection is a significant and critical approach in the domain of digital video processing. It is the foremost critical job of content-based video retrieval and indexing. In this paper, a novel approach for shot boundary detection algorithm based on Differential evolution algorithm (DE) with SVM classifier is proposed. In this method we first calculate the curves difference of U-component histograms as the feature of difference between video frames. In the next step, Slide-Window Mean Filter to filter difference curves and SVM Classifier applying DE to detect and classify the shot transitions. The well-known TRECVID 2005, 2006, and 2007 datasets are used to test the performance of our proposed approach. The result shows the superior performance of our proposed approach, and this method can achieve high recall, precision rate, accuracy, and better computation time. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Employing Parallel Hardware Architectures to Diagnose Sickle Cell Anemia in Real-Time Basis 1

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3rd International Symposium on Signal and Image Processing, ISSIP 2020

作者： Fadhel, Mohammed A. Al-Shamma, Omran College of Computer Science and Information Technology University of Sumer ThiQar Iraq University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

Among the solid components of the human blood, the type that has the largest number of them is well known as red blood cells (RBCs). These cells have flat-round shapes, where their centers are depressed like a doughnut missing its hole. When the cell shape is changed from circular to sickle, then this case is a blood disease named sickle cell anemia (SCA). Based on its number, the dangerous level is obtained. This paper employs parallel hardware architectures to detect the sickle cells and its dangerous level in a real-time basis. These parallel architectures include the field programmable gated array (FPGA) and the graphical processing unit (GPU). In addition, the central processing unit (CPU) as the common serial architecture is also employed for comparison basis in terms of time consuming and power consumption. The circular Hough Transform (CHT) method is employed for detecting the sickle cells. To determine the dangerous level, the number of sickle cells and the number of normal ones are counted. The detection, counting, and classification algorithms are all coded in the Verilog language (for the FPGA) and in the MATLAB software (for the GPU and CPU). The findings have achieved well-behaved performances and acceptable results are obtained. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Parallel architectures

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