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2020 International Conference on Computation, Automation and Knowledge Management, ICCAKM 2020

作者： Mohammed, Amin Salih Barkovska, Olesia Suganthi, S.T. Rosinskiy, Dmytro Oleg, Barabash Dept of Computer Engineering Lebanese French University Erbil Iraq Dept of Software and Informatics Engg Salahaddin University Erbil Iraq Kharkiv National University of Radio Electronics Department of Electronic Computers Kharkiv Ukraine State University of Telecommunications Department of Mathematics Kyiv Ukraine

ISBN: (纸本)9781728106663

The conceptual model of plant cultivation processes automation system for small land parcels was proposed, the system prototype with the functions of data acquisition about weather conditions and soil moisture, which frost forecast method is based on with the use of the perceptron neural network was developed. Development of this system is the actual task because existing solutions are not a single complex and are aimed at a limited amount of issues to be solved, which leads to an increase of the cost of final decision. © 2020 IEEE.

关键词： Data acquisition

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Quality Grading of Beef using Deep Convolutional Neural Network

Quality Grading of Beef using Deep Convolutional Neural Netw...

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HCT Information Technology Trends (ITT)

作者： Aarav Sharma Tanupriya Choudhury Tanmay Sarkar Madhusmita Mohanty Nikunj Bansal Sachi Nandan Mohanty Informatics Cluster School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India CSE Dept. University of Petroleum and Energy Studies (UPES) CSE Dept. Graphic Era Hill University Dehradun Uttarakhand India Department of Food Processing Technology West Bengal State Council of Technical Education Govt. of West Bengal Malda Polytechnic Malda India VIT-AP School of Business (VSB) VIT-AP University Amaravati Andhra Pradesh India School of Computer Science & Engineering VIT-AP University Amaravati Andhra Pradesh India

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy.

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Deep learning integration to inject intelligence in network routing mechanism

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AIP Conference Proceedings 2023年第1期2587卷

作者： Vivek Veeraiah Mohammed Rahmath Karunendra Verma Umesh Pratap Singh Ankur Gupta Malik Bader Alazzam Dept. of R & D Computer Science Adichunchanagiri University Mandya Karnataka India Dept. of Computer Science Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Dept. of Computer Science and Engineering Shri Ramswaroop Memorial Group of Professional Colleges Lucknow India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Mubis Jordan

Deep learning is a type of machine learning that imitates the way humans gain certain types of knowledge. While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. Intelligent Routing (or Skills-based Routing), is a technology contact centers use to gather customer inquiries through voice, digital, or social channels, and then applies rules to route it to the agent best fit to resolve the issue. Research aims to inject intelligence in network routing mechanism using deep learning. Research work is working on routing mechanism to reduce the problems of previous researches. Proposed work increases the speed of routing, and less time consumption. Proposed work is based on deep learning technology for intelligent routing mechanism. The weight of a node route is determined in the first phase by taking into account these parameters. Rewards and penalties are assigned to each node based on its route once it has successfully sent data. Six scenarios and ten associated nodes were used in the route selection simulation. Nodes 1, 2, 3, 4, and 5 form a network in this architecture. This opportunistic network has nodes 6, 7, 8, 9, and 10. Black and blue nodes in the opportunistic network are shown in simulations, respectively. Using the node route table, a node’s connectivity is discovered. The system has become sophisticated enough to make decisions thanks to the usage of neural fuzzy logic. The optimization approaches choose the shortest route to pick a network for a real-world application while taking into account the best solution. The sensing mechanism, as well as animal and crop scouting mechanisms have all been examined in this study. The information would be sent in the form of analogue signals via the various sensor nodes that are linked to the Neuro fuzzy controller. The proposed work would provide flexible and scalable solution to implement opportunistic routing in precision

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THOR-SR: TGF and High-energy astrophysics Observatory for gamma-Rays on board the Space Rider

THOR-SR: TGF and High-energy astrophysics Observatory for ga...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： R. M. Curado Da Silva J. M. Maia J. R. Campos G. Falcão F. Pinheiro M. Abreu M. Caine P. Carmo M. Ferreira C. Francisco J. Gonçalves M. Letra M. Moita R. Moura A. Neves P. Póvoa J. Sousa A. Trindade C. Granja J. Jakubek F. Castanheira LIP - Laboratório de Instrumentação e Física Experimental de Partículas Coimbra Portugal Department of Physics University of Coimbra Coimbra Portugal Physics Department University of Beira Interior Covilhã Portugal Department of Informatics Engineering University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal University of Coimbra Geophysical and Astronomical Observatory Coimbra Portugal Dept. of Physics and Earth Science University of Ferrara Ferrara Italy ADVACAM s.r.o. Prague Czech Republic Active Space Technologies Coimbra Portugal

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

THOR-SR (TGF and High-energy astrophysics Observatory for gamma-Rays on board the Space Rider) mission is a high-energy astrophysics pathfinder mission designed to explore a range of phenomena that includes gamma-ray astrophysics’ emissions, space weather and terrestrial gamma-ray flashes (TGFs). The mission’s scientific payload is based on CdTe and Si detectors with a large field of view (up to 2π sr) provided by a four-layer CdTe stack detector. The detector operates in the 0.1-10 MeV energy range and will be used to perform spectroscopy, imaging, time variability, and polarimetry. THOR prototype performance tests will be performed, presented and discussed. In particular, tests under ESRF polarized beam and ICNAS proton beam facilities, that will allow to validate THOR operation and performance potential during the Space Rider two months flight.

关键词： Particle beams Spectroscopy Observatories Astrophysics II-VI semiconductor materials Extraterrestrial phenomena Space missions

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. Research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. Research work has proposed modified algorithm enhances the security of big data in cloud application against SQL injection, cross script.

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Techno-economic analysis of cognitive radio models in 5G networks

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Procedia computer Science 2020年 175卷 300-307页

作者： Christos Bouras Anastasia Kollia Andreas Papazois Computer Technology Institute & Press ”Diophantus” Patras P.C. 26504 Greece Computer Engineering & Informatics Dept. University of Patras Patras P.C. 26504 Greece

The first commercial products of 5G will be released within 2020 and therefore, it becomes an absolute necessity to research whether the key enabling technologies are advantageous for the operators to invest in. One of the most fundamental technologies Cognitive Radio (CR), since there is not much research in the field. This paper develops a techno-economic framework for the CR technology and is contrasted with an already existing model for Software Defined Networks (SDN). A Sensitivity Analysis (SA) indicates the cost parameters that are the most expensive ones and should be reconsidered for the model’s wide adoption and viability.

关键词： 5G cost models mobile networks SDN cognitive radio

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Age Estimation System Using Deep Residual Network Classification Method 21

Age Estimation System Using Deep Residual Network Classifica...

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21st International Electronics Symposium, IES 2019

作者： Fariza, Arna Mu'Arifin Arifin, Agus Zainal Politeknik Elektronika Negeri Surabaya Informatics and Computer Eng. Dept. Surabaya Indonesia Institut Teknologi Sepuluh Nopember Informatics Engineering Dept. Surabaya Indonesia

ISBN: (纸本)9781728144498

The human face has biometric properties that are important for providing age information because of the aging process of the face. Automatic Age estimation is a difficult problem because the relationship between facial images and age is not very linear. Deep residual network (Resnet) is a neural network convolution architecture that was easier to optimize and can gain accuracy results from a considerably increasing depth. In this paper, we propose a new approach age estimation on convolution neural network (CNN) using the deep residual network (Resnet) model. Through the literature, Resnet achieves superior results when compared with other state-of-the-art image classifications. We compare a new generation of deep residual network called ResNeXt with Resnet and a basic linier regression model *** user UTKFace dataset to evaluate the performance of residual network for age estimation of the range 1-100 years old. The result shows that the ResNeXt-50 (32×4d) architecture achieves a better age estimation results than Resnet-50 and linier regression. © 2019 IEEE.

关键词： Convolution

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Statistical Properties of the Hurst Exponent Estimates for Fractional Levy Motion

Statistical Properties of the Hurst Exponent Estimates for F...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Vadim Shergin Larysa Chala Serhii Udovenko Artificial Intelligence dept. KHNURE Kharkov Ukraine Head of Informatics and Computer Engineering dept. KNUE Kharkov Ukraine

ISBN: (数字)9781728155661

ISBN: (纸本)9781728155678

In this paper, the model of fractional Levy motion is studied. Conventional methods for estimating the Hurst exponent are inapplicable to such processes because of the heavy tails. The method of fractional moments makes it possible to estimate Hurst exponent both for heavy-tailed processes and for processes with long-range dependence. The obtained estimate is simple in software implementation and applicable according to numerical results. Studying the statistical properties of this estimate (such as consistency and unbiasedness proof, mean square error estimating) as well as finding the optimal values of fractional moment is of current interest.

关键词： fractals mean square error methods stochastic processes Mean square error Markov chain Fractals Exponent levy Stochastic Processes Statistical property software implementation

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

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arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Pigmented skin lesions classification using convolutional neural networks

Pigmented skin lesions classification using convolutional ne...

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2019 International Conference on Biomedical Innovations and Applications, BIA 2019

作者： Naronglerdrit, Prasitthichai Mporas, Iosif Perikos, Isidoros Paraskevas, Michael Kasetsart University Sriracha Campus Dept. of Computer Engineering Chonburi Thailand University of Hertfordshire Dept. of Engineering Hatfield United Kingdom University of Patras Computer Engineering and Informatics Dept. Patras Greece Computer Technology Institute and Press 'Diophantus Patras Greece University of Peloponnesse Dept. of Electrical and Computer Engineering Patras Greece

ISBN: (纸本)9781728147543

In this paper we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image pre-processing for natural hair removal and image segmentation for extraction of the skin lesion area. The segmented images were processed by a convolutional neural network classifier. The training process was done by using the Keras and TensorFlow python packets with CUDA supported. The best performance was achieved by a convolutional neural network architecture with three convolution layers and the classification accuracy was equal to 76.83%. © 2019 IEEE.

关键词： Image classification

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