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Optimized Sequential model for Plant Recognition in Keras

IOP Conference Series: Materials science and engineering 2021年第1期1022卷

作者： Shilpi Aggarwal Madhulika Bhatia Rosy Madaan Hari Mohan Pandey Faculty of Engineering and Technology ManavRachna International Institute of Research and Studies Faridabad (Haryana) India Associate Professor Department of Computer Science Engineering Amity University Noida (Uttar Pradesh) India Associate Professor Faculty of Engineering and Technology ManavRachna International Institute of Research and Studies Faridabad (Haryana) India Department of Computer Science Edge Hill University Lancashire United Kingdom

There are huge varieties of floras in the world. Lots of varieties of species are beneficial to the human's life. Plant recognition is a very important task to segregate the huge amount of floras which belong to various categories. Various researchers have applied different approaches to recognize the plant family. Deep learning is a subset of machine learning. This is one of the accepted technologies that automatically extracts features, processes them and yields the best results. Keras is a widely used deep learning framework which is employed in this work. Five different plant species are chosen as samples among the Indian species, namely OcimumTenuiflorum, Sansevieriatrifasciata, Chlorophytumcomosum, Azadirachtaindica, Aloe Vera. These samples are rich in oxygen. From these samples the features like shape, color, texture, corners are extracted. One hot encoding is also applied onto the target values to optimize the results of recognition. The extracted features are fed into the sequential keras model which recognizes the plant species. The accuracy of the training set is 100 percent and the testing set is 96.7percent. Confusion matrix is drawn to show the correctly classified and misclassified samples.

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Applications of Flying Ad-hoc Network During COVID-19 Pandemic

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IOP Conference Series: Materials science and engineering 2021年第1期1099卷

作者： Manisha Devi Sunil Kumar Maakar Deepak Sinwar Mahesh Jangid Poonam Sangwan Scholar Department of Computer Science and Engineering BRCM College of Engineering and Technology Bahal Haryana India Assistant Professor Department of Computer Science and Engineering BRCM College of Engineering and Technology Bahal Haryana India Assistant Professor Department of Computer and Communication Engineering Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Associate Professor Department of Computer Science and Engineering Manipal University Jaipur Dehmi Kalan Jaipur Rajasthan India Assistant Professor Department of Chemistry Government College Hisar Haryana India

The 2019 novel coronavirus (2019-nCoV), with a beginning stage in Wuhan (China), has spread quickly among people living in different nations and has affected more than 6 million lives. Researchers around the globe are trying to device a solution in the form of the vaccine but unfortunately, till date, there is no full-proof vaccine for 2019-nCoV. Most of the countries are adopting prolonged lockdown and social distancing strategies to counter this pandemic. Flying Ad-hoc Network (FANET) on the other hand, can provide several services such as the delivery of essential items, disinfecting common areas, surveillance, traffic monitoring, communication, temperature monitoring, etc. Availing such services using FANETs during Corona Virus Disease (COVID-19) outbreak is like a boon that can minimize the general problems of mankind to some extent during lockdowns. Nowadays, it is foremost required to deploy solutions that can automate the process of availing several services using FANETs and the same is attracting researchers in great demand. This paper has presented a brief survey of several applications of FANETs (especially UAVs and Drones) that can facilitate mankind in coping with the general problems during COVDI-19 outbreak and subsequent lockdowns. We hope that this survey has covered most of the applications of UAVs and can provide new insights into the research community.

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Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Vinay Kumar Baraa Riyadh Altahan Tariq Rasheed Prabhdeep Singh Devpriya Soni Hashem O. Alsaab Fardin Ahmadi Department of Computer Science Dyal Singh Evening College (University of Delhi) Delhi 110003 India Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University College Hilla Babylon Iraquomus.edu.iq Department of English College of Science and Humanities Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabiapsau.edu.sa Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun 248002 Uttarakhand Indiageu.ac.in Associate professor Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida Indiajiit.ac.in Department of Pharmaceutics and Pharmaceutical Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabiatu.edu.sa Addiction and Neuroscience Research Unit Taif University Taif 21944 Saudi Arabiatu.edu.sa Computer Science Faculty University Rana University Kabul Afghanistan

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians’ detection rate was 74.60 percent lower than deep learning’s detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images.

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Renewable Energy Sources Efficient Detection in Triangulation for Wireless Sensor Networks

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： B Barani Sundaram Nunu Srinivas N Kannaiya Raja Manish Kumar Mishra Dhanabal Thirumoorthy NR Reddy Professor & Associate Dean-ICT Department of Computer Science BuleHora University Bulehora Ethiopia Assistant Professor School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Professor Department of Computer Science School of Informatics and Electrical Engineering Ambo University IOT Campus Ambo Ethiopia Department of Computer Science University of Gondar Gondar Ethiopia Lecturer BuleHora University Bulehora Ethiopia Assistant Professor Department of Computer Science Mekelle University Mekelle Ethiopia

It is important for wireless sensor networks (WSNs) to determine whether hubs can be restricted, called localizability recognition. This progression is important for confining hubs, achieving minimal organizations of effort and distinguishing critical in field-based applications. Because of their high calculation and communication costs, centralized graph algorithms are meaningless to an asset-restricted WSN, whereas distributed methodologies can skip a large number of hypothetically locatable hubs in a resource-limited WSN. In this document we are proposing a efficient and successful distributed methodology to solve that address of issue. In addition, we demonstrate our calculation accuracy and analyze the reasons why our calculation will discover increasingly localizable hubs while requiring less established area hubs. The paper introduces a new RSSI-based localization approach triangle as well as centroid placement, utilizing triangle as well as centroid approach to minimize RSSI measurement error. Modelling simulations show that this algorithm can significantly increase the precision of the location compared to the trilateration.

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Contactless Attendance Management System using Artificial Intelligence

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Journal of Physics: Conference Series 2021年第1期1714卷

作者： M Rajamanogaran S Subha S Baghavathi Priya Jeevitha Sivasamy Data Science Consultant Chennai Tamil Nadu India Associate Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Professor Department of Information Technology Rajalakshmi Engineering College Chennai Tamil Nadu India Research Scholar Department of Computer and Information Science Annamalai University Chidambaram Tamil Nadu India

Attendance monitoring is one of the vital administrative processes in all educational institutions and organizations. A well-structured system will enable the institutions to have an increasing growth. It helps the students and teachers in all ways to have a good progress in the attendance, thereby reducing the teachers' time and effort. In physical classrooms, traditional method of calling the students names and marking their presence/absence is the routine process followed regularly. Attendance monitoring system is proposed using artificial intelligence. Firstly, a database containing the facial images of the students in a particular class is constructed. Knowledge gained using Convolutional Neural Network (CNN) is reused in a perfect manner using transfer learning. This system is designed to improve the students' engagement time inside the classroom, to communicate to the parents frequently, to avoid proxy attendance and to generate detailed reports for future reference.

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Design and Implementation of a Control, Monitoring and Supervision System for Train Movement Based on Fixed Block Signaling System with AVR Microprocessor

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IOP Conference Series: Materials science and engineering 2020年第1期671卷

作者： Mohammad Ali Sandidzadeh Farzaad Soleymaani Shahrouz Shirazi Associate Professor School of Railway Engineering Iran University of Science & Technology Ph.D. Student School of Railway Engineering Iran University of Science & Technology MSc. School of Railway Engineering Iran University of Science & Technology

Railway signaling and control systems are vital for the safe control of transport processes in rail traffic. Today, industry uses many processes, such as railway signaling systems, that must be controlled. Manufacturers of such systems have tried to update their products in line with modern industrial control technologies. In this paper, the possibility of using the common industrial automation equipment for a signaling system are investigated. Different parts of the signaling system, including wayside equipment, field element controls (interlocking and object controller) and supervision systems were specified. Then, the interlocking logic was extracted based on a fixed block signaling system. Control, monitoring and supervision systems were designed with an AVR microcontroller and then implemented on printed circuit board. Finally, the outputs of the system and its results with various railway lines, based on chosen layout, were analyzed and its applications identified. These applications include wayside equipment monitoring, the training of traffic control operators, and movement safety tests of the line before setup of the main system.

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Preface

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Journal of computer science & technology 2020年第3期35卷 491-492页

作者： Shi-Min Hu Ying He Belen Masia Professor Department of Computer Science and TechnologyTsinghua UniversityBeijing Associate Professor School of Computer Science and EngineeringNanyang Technological University Singapore Assistant Professor Department of Computer ScienceUniversity of ZaragozaZaragoza

The Computational Visual Media(CVM)conference series is intended to provide a major international forum for exchanging novel research ideas and significant computational methods that either underpin or apply visual *** primary goal is to promote cross-disciplinary research to amalgamate aspects of computer graphics,computer vision,machine learning,image and video processing,visualization and geometric *** main topics of interest to CVM include classification,composition,retrieval,synthesis,cognition and understanding of visual media(e.g.,images,videos,3D geometry).

关键词： computer graphics visual

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Compressing Biosignal for Diagnosing Chronic Diseases

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Journal of Physics: Conference Series 2021年第1期1998卷

作者： P. Ithayarani A. Jainul Fathima Associate Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation (KLEF) Deemed to be University Green Fields Vaddeswaram Guntur District A.P. India Assistant Professor Department of Information Technology Francis Xavier Engineering College Tirunelveli TN. India

Electrocardiogram, electromyogram, electroencephalogram are the foremost required vital signs for diagnosing chronic diseases like sleep disorder, mood disorder, epilepsy etc., which demands long-term monitoring. A sensor based wearable system which is enabled with internet technology, supports the continuous recordings of these vital signs without troubling the patient's daily activities. And the wearable hub is responsible for collecting the readings of bio signals from multiple micro-sensor nodes deployed around the body which creates the short range of communication and forward to the observer. These continuous monitoring increases the signal transmission cost and declines the battery life of wearables. So, the observed multiple bio signals can be compressed jointly than individually before sending, at an edge level. This paper proposes transfer learning based multimodal convolutional denoising autoencoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with better quality score than by randomly initialized weights. The proposed work achieves compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in case of consuming multiple sensors. And the experimental result proves that the computation cost is low in multimodal compression than in unimodal compression.

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Multi Path Routing Technique Based RRP for P2P Network

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IOP Conference Series: Materials science and engineering 2020年第1期994卷

作者： Mary Subaja Christo R. M. Bommi K. Mariyappan Associate Professor Department of Information Science and Engineering Faculty of Engineering Technology Jain University Bangalore India Associate Professor Mechatronics Engineering Department Chennai Institute of Technology Kundratur India Assistant Professor Department of Computer Science and Engineering Jain University Bangalore India

All articles must contain an abstract. Peer to Peer (P2P) network is useful for developing applications and downloading files from the internet. In the existing P2P networks, communication is initiated and supported by peers. Security issues constitute the main challenge in the design of reliable communication techniques for P2P networks. In the past, Rumor Riding Protocol was used in distributed systems for performing coordination and communication. However, the existing Rumor Riding Protocol is used flooding technique to forward the response to the initiator it may leads high network traffic to avid this I have Enhanced Multi path forwarding technique to Rumor Riding Protocol and also enhancement with security features to make them suitable for downloading applications in P2P networks. In this method, it enhances the Rumor Riding Protocol with security features by including a Cryptographic Puzzle, a Challenge Question scheme using trust modeling and an intelligent beta reputation model for secured communication in P2P networks. For this purpose, five new techniques are proposed in this research work, all of which aim at enhancing the security of communication using Rumor Riding Protocol in the P2P networks.

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Innovative Task Scheduling Algorithm in Cloud Computing

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IOP Conference Series: Materials science and engineering 2020年第2期981卷

作者： S. Magesh Kumar V. Auxilia Osvin Nancy A Balasundaram Seena Naik korra D Kothandaraman E Sudarshan Associate Professor Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Assistant professor Department of IT S. A. Engineering College Chennai Tamil Nadu India Assistant Professor Center for Cyber Physical Systems (CPS) School of Computer Science and Engineering Vellore Institute of Technology (VIT) Chennai Tamil Nadu India Associate Professor School of Computer Science and Artificial Intelligence S R University Warangal India Associate Professor Sumathi Reddy Institute of Technology for Women Warangal India

This work concentrates around the task scheduling depends on QoS and imperative of desire. Under nature of vibrant cloud computing, effectiveness improving of scheduling of task and load balancing are interminable issues. For clients, be that as it may, it's progressively imperative to meet their necessities of QoS. This paper identifies with an improvement decency intention depends on new Berger's model under the earth of vibrant cloud computing. As indicated by the distinctive kind of task scheduling, we portray the need of decency, proficiency and the Balance among advantage and reasonableness separately. We recompile the CloudSim and recreate the three undertaking task scheduling above based on expanded CloudSim individually. The exploratory outcomes show that this calculation portion not just meet the guideline of offering need to profit with because of thought to decency, yet additionally address clients' issues of orchestrated QoS.

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