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International Conference on Communication and Electronics Systems (ICCES)

作者： Munugapati Bhavana Koppula Srinivas Rao Gagan Kumar Koduru K.Vijay Krupa Vatsal S. Parvathi Mogiligidda Sravani Dept. of Computer Science & Information Technology MLR Institute of Technology Affiliated to JNTU Hyderabad Department of Computer Science & Engineering MLR Institute of Technology Affiliated to JNTU Hyderabad MLR Institute of Technology Hyderabad Department of Computer Science & Engineering Malla Reddy University Hyderabad Dept.of Computer Science & Information Technology MLR Institute of Technology Hyderabad Department of Information Technology MVSR Engineering College

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment.

关键词： Measurement Ethics Machine learning algorithms Plagiarism Machine learning Complexity theory

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On the Enhancement of Energy Efficiency of Wireless Rechargeable Sensor Networks through Mobile Charger Scheduling

On the Enhancement of Energy Efficiency of Wireless Recharge...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Md. Kamaruzzaman Abhijit Chandra Md. Azharuddin Dept.of Robotics and Automation The Neotia University Kolkata India Dept. of Instrumentation and Electronics Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Aliah University Kolkata India

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme.

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Monolithic 3D Printed Micro-Optofluidic Device for Two-phase Flow Monitoring and Bioapplications

Monolithic 3D Printed Micro-Optofluidic Device for Two-phase...

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Electric And Power engineering (EPEi), IEEE International Conference And Exposition On

作者： Lorena Saitta Emanuela Cutuli Giovanni Celano G Claudio Tosto Francesca Guarino Gianluca Cicala Maide Bucolo dept.of Civil Engineering and Architecture University of Catania Catania Italy dept. of Electrical Electronic and Computer Science Engineering University of Catania Catania Italy dept. of Biomedical and Biotechnological Science University of Catania Catania Italy

ISBN: (数字)9798350356199

ISBN: (纸本)9798350356205

In this work, a biocompatible 3D printed micro-optofluidic (MoF) device designed for monitoring two-phase flows is presented. It was manufactured using the Projection Micro-Stereolithography (PμSL) 3D printing technique combined with a biocompatible resin. This device successfully monitored both an air-water slug-flow and a two-phase mixture of micrometric eukaryotic yeast cells (Saccharomyces cerevisiae) suspended in a phosphate-buffered saline (PBS) solution. Its working principle relies on the absorption phenomenon, since different light transmissions are correlated to either the fluid or the cells interfering with a laser beam in a microchannel section. To accomplish this, the MoF device was designed with two micrometric slots for the insertion of optical fibers to capture the light signal. Both the micro-optical and microfluidic components, each 200 μm in size, were integrated into the MoF device as a monolithic structure. A wide experimental campaign was performed on the MoF device investigating different flow rates and cells concentrations. The optimal operative condition for achieving the most robust working ability were determined using a Design of Experiments (DoE) approach.

关键词： Performance evaluation Fluids Biomedical optical imaging Voltage measurement Cells (biology) Three-dimensional printing Stability analysis Real-time systems Biomedical monitoring Monitoring

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Reduction of peak ground velocity by nonlinear soil response–Ⅱ:excitation by a P-wave pulse

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Earthquake engineering and engineering Vibration 2021年第4期20卷 823-841页

作者： Vlado Gičev Mihailo D.Trifunac Maria I.Todorovska Dept.of Computer Science Univ.Goce DelčevTošo Arsov 142000ŠtipR.Macedonia Dept.of Civil Eng. Univ.Southern CaliforniaLos AngelesCalifornia 90089-2531USA Department of Civil Engineering Tianjin UniversityTianjin 300350China

We study the reduction of peak velocity on the ground surface of a soil valley caused by loss of wave energy by large nonlinear strains and strain localization inside the valley,for excitation by a half-sine P-wave *** study is a follow up to our previous study of out of plane response for excitation by an *** this paper,we consider the in-plane response,and assume that the soil material does not support tension,but the normal stress at a point in the soil can be compression(negative)or zero.A point in the soil with zero stress behaves as a stress-free point,it does not transmit normal stress and appears as a crack *** of this,along with the nonlinear response associated with compression and shear,the in-plane response in this study is more complex than that of the out-of-plane SH *** study the interplay of two opposing effects:(i)jump in impedance from a higher value(half-space)to a lower value(valley),which amplifies the linear motions at the free surface of the valley,and(ii)the occurrence of nonlinear zones in the valley,which reduce the motion at the valley surface.

关键词： nonlinear soil response non-linear seismic P waves energy dissipation by yielding soil strain localization

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Human Activity Recognition using ShuffleNetV2 Model

Human Activity Recognition using ShuffleNetV2 Model

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Intelligent Computing and Control for engineering and Business Systems (ICCEBS)

作者： Shanufa Nazrin P S J.V. Bibal Benifa Anu K. P Aiswarya Vijayakumar dept. of Computer Science St.Joseph's college (university of calicut) Thrissur Kerala dept.of Computer Science and Engineering Indian Institute of Information Technology kottayam Kerala India

HAR is specified as the crucial task within the field of computer vision and artificial intelligence. Main aim of HAR is to observing and labelling different human activity images of distinct data sources by machines such as computer. HAR has applications in various fields. CNN model is an appropriate model for HAR task, due to their ability to automaticaly acquire relevant spatial features, capture hierarchical representations, and handle input of various sizes and types. So it is utilized for developing HAR model. This model can detect objects and also can classify images. The model contains wide ranging catogories. ShuffleNetV2 is a particular architecture within it, mainly for efficient and lightweight deep learning task. This paper develops an intelligent HAR using ShuffleNetV2 model which acquired an accuracy about 69.52 percentage.

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Intelligent Heart Rate Classification with Adaptive Neuro-Fuzzy Inference System Approach 4

Intelligent Heart Rate Classification with Adaptive Neuro-Fu...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Seva, Banothu Kumhar, Bhupchand Gupta, Jyoti Patel, Sharda Basha, Shaik Khaja Mohiddin Jain, Archi Mlr Institute of Technology Department of Cse Hyderabad India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal462044 India Indore Institute of Science and Technology Dept. of Mathematics Madhya Pradesh Indore India Gyan Ganga Institute of Technology & Sciences Dept of Cse Jabalpur India Narasaraopeta Engineering College Dept.of Cse India Ggits MP Jabalpur India

ISBN: (纸本)9798331518981

The human heart is considered among the most crucial internal organs found in the body. An ECG signal is defined as the widespread name given to any electrical signal that is generated by the circulatory system*. By using the ECG signal, one can estimate anomalies present in a heart. This study is aimed at grouping the electrocardiogram (ECG) signals, and thereby using an adaptive neuro fuzzy inference system (ANFIS) algorithm. In this study, the ANFIS which is associated with the back propagation approach is used in order to perform the categorising task. ANFIS can be developed from the integration of fuzzy logic which is a qualitative technique with the neural network aspect in allowing adaptability. Attribute selection is done before the classification exercise is conducted. Four different types of ECG beats can be retrieved from the datasets available at PhysioBank. Classification of the cardiac signals is done employing four ANFIS classifiers. The fifth ANFIS decoder is used to get a higher discrimination ratio in order to classify the electrocardiograms accurately. © 2024 IEEE.

关键词： Fuzzy neural networks

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Prediction of COVID-19 Through Chest X-Ray Images Employing Various Machine Learning Techniques 1

Prediction of COVID-19 Through Chest X-Ray Images Employing ...

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1st International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

作者： Saheb, Shaik Khasim Narayanan, B. Rao, Thota Venkat Narayana Annamalai University Dept.of Cse Tamilnadu Chidambaram India Sreenidhi Institute of Science and Technology Department of Cse Hyderabad India Annamalai Univeristy Computer Science and Engineering Wing D.D.E Annamalai Nagar Tamilnadu Chidambaram India Sreenidhi Institute of Science and Technology Department of Computer Science and Engineering and HoD-CSE Iot Telangana Hyderabad India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

The COVID-19 is the most infectious disease which is recently discovered. The COVID-19 pandemic has led to excruciating loss to human life and it also caused mild to severe respiratory illness, including death. Detecting the infected patients and taking special care is crucial in fighting covid-19. Radiography and Radiology images are used to diagnose the patients. These are the fastest ways to identify COVID-19 disease. It is observed that patients affected with COVID-19 have specific abnormalities in their chest radiograms. Initially there were few limited set of CT images are available publicly in performing research. Board-certified radiologist role is to perform identification of images exhibiting COVID-19 disease. Chest CT scans are helpful to diagnose COVID-19 disease in individuals. COVID-19 directly shows impact on lungs and it damages and the tiny air sacs. In this paper we have used deep transfer learning models Residual Network (ResNet50) and VGG19 (Visual Geometry Group) to predict the disease at earlier stages. These models obtained a specificity rate of 90% and achieved a highest sensitivity rate of 98 %. In addition to sensitivity and specificity rates ROC curve, average prediction and confusion matrix of each model are presented in the papers. While this achieved performance is very encouraging if we have large set of COVID-19 images then it may give more reliable estimation of accuracy rates. © 2022 IEEE.

关键词： COVID-19

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An Efficient Machine Learning Model for Identification of Seagrasses through Morphometrics

An Efficient Machine Learning Model for Identification of Se...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Ashwani Kumar Dubey Jibi G Thanikkal E. Dilipan Puneet Sharma Manoj Kumar Shukla Dept.of Electronics and Communication Engineering ASET Amity University Uttar Pradesh Noida UP India Dept.of Computer Science and Engineering ASET Amity University Uttar Pradesh Noida UP India Center for Marine Omics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India School of Computer Science & AI SR University Warangal Telangana India

Seagrasses are marine flowering plants capable of completing their life cycle underwater. Globally, 72 species of seagrasses have been identified, including 15 species from India. Artificial intelligence/ Machine learning techniques are gaining wide use in many fields, and their applications in marine sciences are still in the initial stage. Considering this, a study is attempted to identify seagrass species through Artificial intelligence/ Machine learning techniques. Since the characteristics of seagrass vary depending on location and other environmental factors, identifying seagrass species is regarded as a difficult task. In the present study, a known seagrass species is taken as a reference and its leaf morphological features are considered for shape description and deep learning-based identification. Leaf morphometrics of seagrass species are drawn to develop the databases. Morphological feature extraction algorithm, descriptor generation algorithm, and deep learning-based identification are applied for species identification. Existing artificial intelligence/ machine learning models use image pixels as input to the deep learning model. However, this is the first attempt to recognize seagrasses through their morphological properties. Here, the leaf shape property is utilized for seagrass identification. The proposed machine learning model received 60% accuracy during testing. Additional investigation was possible with the same algorithm for future seagrass species identification. Utilizing Artificial intelligence/ Machine learning algorithms may lead to identifying new seagrass species.

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Fuzzy Logic-based Energy Management for WSNs in Smart Grid Applications

Fuzzy Logic-based Energy Management for WSNs in Smart Grid A...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： K. Vengatesan R. Vijayalakshmi Sayyad Samee Neha Chopade Shilpa Mahalle K. Bashkaran Department of AIDS Sanjivani University Kopargaon Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Dept.of Computer Science Al Sadara School Al Ain Abu Dhabi UAE SIES College of Management Studies Kongunadu College of Engineering and Technology Thottiam

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Fuzzy Logic-Based Energy Management (FLBEM) is an innovative approach for optimizing energy consumption in Wireless Sensor Networks (WSNs) utilized in Smart Grid applications. This technique employs fuzzy logic principles to dynamically adjust network parameters, such as transmission power and data sampling rates, to enhance energy efficiency while maintaining reliable communication. By intelligently adapting to network conditions and environmental factors, FLBEM extends the operational lifetime of sensor nodes and ensures the stability of the WSN, thus facilitating real-time monitoring and control of Smart Grid systems. The proposed FLBEM approach encompasses a comprehensive framework that includes the design of fuzzy logic controllers, the development of energy-aware routing protocols, and the integration of renewable energy sources for powering sensor nodes. Simulation results demonstrate significant improvements in energy efficiency, network lifetime, and overall performance, validating the effectiveness of the FLBEM strategy in the context of Smart Grid applications. As energy management remains a critical concern for WSNs, the implementation of FLBEM offers a promising solution to address the challenges of energy conservation and sustainability in modern power systems.

关键词： Fuzzy logic Wireless sensor networks Renewable energy sources Power system stability Energy efficiency Stability analysis Smart grids Power system reliability Reliability Sustainable development

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Evaluation of Block-Chain Transaction Accuracy using Neural Network Model 2

Evaluation of Block-Chain Transaction Accuracy using Neural ...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Veeraiah, Vivek Suthar, Vishal Reddy, Algubelly Yashwanth Dahiya, Omdev Azam, Mohammed Kumbhkar, Makhan Adichunchanagiri University Dept. of R&d Computer Science Karnataka Mandya India Government Polytechnic College Dept. of CSE Rajasthan Jalore India Sree Dattha Group of Institutions Dept.of CSE Telangana Greater Hyderabad India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Shri Jagdishprasad Jhabarmal Tibrewala University Dept. of ECE Rajasthan Jhunjhunu India Christian Eminent College Dept. of Computer Science MP Indore India

ISBN: (纸本)9781665437899

Block chain is known as distributed ledger technology (DLT). A distributed ledger, near real-time updates, chronological and time-stamped, cryptographically sealed, and programmable contracts are the core aspects of Block chain Technology. Brain cells, known as biological neurons, are used to implement deep learning in Neural Networks. A consequence of this is the implementation of deep learning by means of neural networks with several hidden layers. A lot of the recent work in financial transactions has focused on the intersection between block chain and artificial intelligence (AI). Artificial neural networks of this type are included. AI can help to lower the carbon impact of block chain technology. The cost and energy consumption of mining operations will be reduced significantly if AI machines replace human miners. © 2022 IEEE.

关键词： Distributed ledger

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