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Classification of Malignant Melanoma using Convolutional Neural Networks

Journal of Physics: Conference Series 2021年第1期1916卷

作者： V Vijeya Kaveri V Meenakshi K A Saran Karthik S Shri Raam Professor Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Assistant Professor Department of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology India UG Student Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Human cancer is one of the world's most deadly diseases caused due to genetic disruption of skin cells and several molecular mutations. Skin cancer remains the most predominant form of cancer in human beings. The major goal is to detect skin cancer in early stages by research and analyse it using various techniques such as segmentation and feature extraction. The diagnosis of malignant melanoma skin cancer is done by dermatologist by examining skin and physical biopsy to determine the accurate stage of melanoma. It is developed because of high accumulation of melanin in the dermis layer of the skin. ABCD law is used in along with dermoscopy technology to detect malignant melanoma skin cancer. Image Acquisition Technique, pre-processing, segmentation, distinguishing function for skin Feature Selection, which specifies lesion characterization and classification methods are all conducted in this project for melanoma skin lesion characterization. We used symmetry detection, border detection, colour and diameter detection, as well as feature extraction to remove texture based features using a digital image processing technique. The deep Neural Network was proposed here to characterize the benign or malignant stage.

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Sales prediction using machine learning approaches

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AIP Conference Proceedings 2021年第1期2387卷 140038页

作者： K. Saraswathi N. T. Renukadevi S. Nandhinidevi S. Gayathridevi P. Naveen Assistant Professor (Sr.G) Computer Technology-UG Kongu Engineering College India Assistant Professor Department of Computing Coimbatore Institute of Technology India UG Student BSc (Information Systems) Kongu Engineering College India

For the successful business, several factors are considered and prediction is made for the sales of the product. Here, the sales prediction is proposed to forecast the sales of Rossamann stores using machine learning algorithms. Sales forecasting is done by analyzing customer purchasing behaviour and it plays an important role in modern business intelligence. Forecasting future sales demand is key to business and business planning activities. Forecasting helps business organizations to make improvements, to make changes to business plans and to provide a stock storage solution. Forecast is determined by the use of data or information from past works and the consideration of recognized feature in future. Sales forecasting plays a vital role in strategic planning and market strategy for every company to assess past and present sales statistics and predict potential results. Overall, accurate sales forecasting helps the company to run more productively and efficiently, to save money on forecasts or predictions. In the proposed study, the linear regression and logistic regression model are analyzed and Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) are trained and tested for our dataset. The data is processed to select the features and extract those features. Accurate projections make it easier for the shop to boost demand growth and a higher degree of sales generation. It produces better prediction rate.

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ResNet:A convolutional Neural Network for detecting and diagnosing of coronavirus pneumonia

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IOP Conference Series: Materials Science and engineering 2021年第1期1084卷

作者： R. Angeline R. Vani Assistant Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Professor Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India

In this paper, ResNet a Convolutional Neural Network for detecting and diagnosing the lung disease Covid-19 pneumonia infection automatically. For identifying, Chest X-rays are widely used for diagnosis of pneumonia disease which affects the lungs. This paper provides an approach to detect and diagnose Covid-19 pneumonia and classify the chest X-ray images into two classes either Covid-19 pneumonia or normal utilizing CNN. This is done by training the CNN to differentiate between the normal and pneumonia chest X-ray images using a deep learning platform Pytorch. Image preprocessing technique has been applied in order to enhance the image accuracy. Python and OpenCV have been used.

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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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Performance Evaluation with Multivariable and Multiloop control for a class of square MIMO systems

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IOP Conference Series: Materials Science and engineering 2021年第1期1084卷

作者： S Sudhahar C Ganesh Babu D Sharmila Assistant Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor Professor Bannari Amman Institute of Technology Department of Electronics and Instrumentation Engineering Erode Tamilnadu India Professor Jai Shriram Engineering College Department of Computer Science Engineering Erode Tamilnadu India

Two approaches (centralized and decentralized) for designing a multi-input, multi-output (MIMO) tracing/regulating process are described within article. The vast popular of industrial process control applications are whist focused on multi loop controllers, ignoring the feedback control attainment with advantages of centralized multivariable controllers. Due to their single loop nature, the plant interactions that are merely in use keen on relation in the controller tuning process cannot be suppressed by decentralized controllers. In many situations, therefore it would be beneficial to delimit the detrimental consequences in pairing among inputs and outputs of the closed loop system under certain context. The centralized model predictive controllers (MPC) and decentralized/multi-loop PI controllers are designed. In terms of integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Absolute Error (ITAE), the output of both controllers is then compared. The results of the simulation showed that the MIMO MPC is better than the other suggested control schemes. The projected central controllers minimize interactions superior than the multi loop controllers that have recently been published.

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A Permissioned Blockchain System for Secure Multiparty Computation

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

作者： S Garg R Vashisht Software Engineer DLT Labs Hyderabad India Assistant Professor Computer Science and Engineering Department Ajay Kumar Garg Engineering College Ghaziabad India

Permissioned blockchain is the blockchain network that requires access to be part of the network. Participant's actions are governed by the control layer that runs on top of the blockchain. This type of blockchains is preferred by individuals who need role description, identity, and security within the blockchain. Secure multi-party computation (MPC) is a part of cryptography that involves the modeling of procedures for two or more participants who want to work together. These participants involve sharing of input and required computational data for a particular function without sharing their confidential data actively to each other and achieving a common goal which is beneficial to both as the outcome achieved is only revealed to the participants and is highly required for their functional purpose. In this work, SPDZ (Speedz) implementation is explored leveraging additive secret sharing on the private blockchain (Hyperledger fabric). SPDZ protocol is chosen over any other computational protocol as it is highly secured from any active deceptive n-1 participant among the n participants. In this work, a backend is developed that uses a fabric SDK *** library that interacts with the Hyperledger Fabric network. The proposed solution is shown through a demonstration. This paper concludes that for business-to-business scenarios, using SPDZ protocol on permissioned blockchain provides more security against adversaries as permissioned blockchain provides transparency over the participants of the network. As a result, permissioned blockchain is a more secure choice for enterprises to compute confidential data rather than permissionless blockchain.

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Understanding Hadoop Framework Through Single-Node Cluster Installation

Understanding Hadoop Framework Through Single-Node Cluster I...

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International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC)

作者： V. G. Anisha Gnana Vincy T. Karthija J. Sunil Assistant Professor Department of Computer Science and Engineering V V College of Engineeing Tisaiyanvilai India Assistant Professor Department of Mechanical Engineering V V College of Engineeing Tisaiyanvilai India

ISBN: (纸本)9781538694831

Hadoop is a Java-based open source programming model that becomes a pillar in recent distributed computing by providing massive storage and multiprocessing of data. Hadoop becomes a simple and easy to implement programming model through the usage of commodity hardware. Hadoop open-source became a foundation for massive parallel processing of big data which includes scientific analytics, e-commerce data and sales planning, and processing enormous volumes of sensor from various sensors. Since Hadoop plays vital role in distributed parallel processing of big data, it is good to know the technical details behind the hadoop framework. This paper focus on detailing the necessary steps for the successful implementation of a Hadoop single-node cluster in a individual computer which provide a foundation for the new hadoop users.

关键词： Big Data Java parallel processing public domain software storage management PARALLEL PROCESSING (computerS) big data open source software Store management Java Java programming language Sensors

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A Study On Virtual Intelligence

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Journal of Physics: Conference Series 2021年第1期1770卷

作者： R. Sathya Bama Krishna D. Anto Praveena N Nazhath Nafizza J Naveena Ramesh Vardhini Assistant Professor Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai 600119 India Student Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai 600119 India

Virtual Intelligence can be otherwise known as digital intellect. VI is one of the developing technologies of this decade. It is the result of union of two technologies that are swiftly shooting up. They are Virtual Reality and Artificial Intelligence. While Artificial Intelligence will make machines react like an individual, VR will create an imaginary computer universe. This tech intends in transforming machines more like people. Using this VI technology, one can feel virtual world of their desire. This technology is utilized in many fields. Virtual Intelligence will integrate the mode of teaching. This will boost the gaming sector and will play a role in automation.

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Application of certain Third-order Non-linear Neutral Difference Equations in Robotics engineering

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Journal of Physics: Conference Series 2021年第1期1831卷

作者： S Sindhuja J Daphy Louis Lovenia A P Lavanya D Darling Jemima G Jayabarathy Research Scholar Department of Mathematics Karunya Institute of Technology and Sciences Coimbatore India. Professor Department of Mathematics Karunya Institute of Technology and Sciences Coimbatore India. Assistant Professor Department of Mathematics Sri Krishna college of Engineering and Technology Coimbatore India. Assistant Professor Department of Computer Science and Engineering Sri Ramakrishna Engineering college Coimbatore.

We have obtained some results on oscillatory behavior of third order nonlinear neutral difference equations of the form where β and γ are odd integers with γ ≥ 1. Example is provided to illustrate the results.

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Design and Analysis of an Efficient Multi-Relational Decision Tree Learning Algorithm

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

作者： Chhutten Singh Yadav Abhishek Kumar Ankit Kumar Pankaj Dadheech School of Management Studies Graphic Era Hill University Dehradun Uttarakhand India Assistant Professor Department of Computer Science & IT Jain (Deemed to be University) Bangalore India Assistant Professor Department of Computer Science & Engineering Swami Keshvanand Institute of Technology Management & Gramothan Jaipur Rajasthan India

In today's day of modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. The research in Knowledge Discovery in Databases has been primarily directed to attribute-value learning in which one is described through a fixed set tuple given with their values. Database or dataset is seen in the form of table relation in which every row corresponds to an instance and column represents an attribute respectively. In this paper a New framework is introduced a much more sophisticated and deserving approach i.e., Hybrid Multi-Relational Decision Tree Learning Algorithm which overcomes with Exiting technology drawbacks and other anomalies. Result show that Hybrid Multi- Relational Decision Tree Learning Algorithm provides certain methods which reduces its execution time. Experimental results on different datasets provide a clear indication that Hybrid Multi-Relational Decision Tree Learning Algorithm is comprehensively a better approach.

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