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

作者： Ruchi Garg Poonam Gupta Amandeep Kaur Assistant Professor Electronics and Communication Engineering Department M.M.(Deemed to be University) Mullana Ambala Haryana India Assistant Professor Computer Science and Engineering Department M.M.(Deemed to be University) Mullana Ambala Haryana India Associate Professor Computer Science and Engineering Department M.M. University Sadopur Ambala Haryana. India

Block chain technology had gained popularity because of its use in crypto-currencies like bitcoin. Today uses of blockchain are growing in number of areas like banking, industries, health centers and even security of IOT. Moreover, the use of IOT is growing exponentially every year with its aim in 5G technologies like e- health, smart homes, distributed intelligence etc. but it faces challenges in security and privacy. The privacy of a user data is at a risk because of its (i.e. IoT) centralized client-server model. This centralized approach of the server poses a serious vulnerability to the data security. This data at the server attracts the attackers to enter into the network and invade through the data and schedule attacks or inject a malware. It indicates that the central architecture of IoT possess a compromised confidentiality, integrity, and security of data which disrupts its use as the widespread adoption of this technology. Therefore, it is essential to evade the hostile centralized server architecture for IoT to enhance its security. It implies a need for decentralized architecture to maintain the data. The data can be kept at the different users without any central control with the help of blocks as suggested by Blockchain. This paper addresses the various security issues in IOT and how block chain helps in solving these issues.

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Soft Computing Techniques for Clustering in WSN

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

作者： Amandeep Kaur Poonam Gupta Ruchi Garg Associate Professor Computer Science and Engineering Department M.M. University Sadopur Ambala Haryana India Assistant Professor Computer Science and Engineering Department M.M. (Deemed to be) University Mullana Ambala Haryana India Assistant Professor Electronics and Communication Engineering Department M.M. (Deemed to be) University Mullana Ambala Haryana India

The large Wireless Senor Networks (WSNs) comprises Sensor Nodes (SN) in some hundreds with sensing and communication capabilities. Major limitation of WSN is limited energy source of SN. Clustering is one of energy efficient method for energy conservation in WSNs. Clustering is the process to distribute SN in various logical groups known as cluster. Each cluster bears a Cluster Head (CH), who is responsible for transmitting aggregated data of its cluster to Base Station (BS). The literature is enriched by various clustering protocols proposed with the assistance of soft computing. This paper, presents the survey of soft computing paradigm-based clustering protocols.

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Consumer product preference prediction towards online shopping

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

作者： S Deepa R Suguna P Sathishkumar C Jamunadevi E N Vidhya Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai India

Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.

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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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Terrain identification and land price estimation using deep learning

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AIP Conference Proceedings 2021年第1期2387卷

作者： K. Kousalya B. Krishnakumar A. S. Aswath P. S. Gowtham S. R. Vishal 1Professor Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu 638060 India 2Assistant Professor Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu 638060 India 3UG Scholar Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu 638060 India

The real estate market is becoming one of the most competitive in terms of pricing and same tends to vary significantly based on various factors. This paper focuses on identifying the type of land and estimating the price using convolutional neural network. Deep learning algorithms exhibit marvelous performance over conventional machine learning algorithms identifying the complex patterns. In this paper two Convolutional Neural Network (CNN) models are used. One is self-proposed model and another is ResNet152V2 model. Models are trained using aerial land image dataset. The ResNet152V2model showed high accuracy compared to the self proposed model. This system helps the land owner to acquire some basic essential details about the land.

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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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Defect Detection of Industrial Products Using Image Segmentation and Saliency

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

作者： P Anantha Prabha M Bharathwaj K Dinesh G Hari Prashath Assistant Professor Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India UG Student Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India

The manufacturing industries have been searching and developing new solutions to increase the product quality and to decrease the time taken and costs of production. Defect detection methodologies consume much time in manufacturing and manual inspections for quality enhancement. The existing systems cannot handle the data other than the trained ones as they followed the comparison process with the dataset which is of more time consuming and lack of effective depth representation. In the proposed system, multi scale saliency defect detection algorithm is implemented to obtain the boundaries and range of defect in the surface of industrial products. The defect in the products can be detected using pre-processing defect image with color channels, detecting uneven illumination and post processing the defect image thereby splitting out the defect part from original image with edge detection and contours. Hence the output will be of more robust and accurate comparing to the existing systems.

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Secure data sharing for mobile cloud computing using RSA

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

作者： M Arumugam S Deepa G Arun P Sathishkumar K Jeevanantham Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Information Technology MP Nachimuthu M Jaganathan Engineering College Chennimalai India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai Erode India

Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.

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An empirical analysis of machine learning frameworks for digital pathology in medical science

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Journal of Physics: Conference Series 2021年第1期1767卷

作者： S.K.B. Sangeetha R Dhaya Dhruv T Shah R Dharanidharan K. Praneeth Sai Reddy Assistant Professor(Sr.G) Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai-600026 India Associate Professor Department of Computer Science and EngineeringKing Khalid UniversityKingdom of Saudi Arabia UG Student Department of Computer Science and Engineering SRM Institute of Science and TechnologyVadapalani CampusChennai-600026 India

Digital pathology is a technology that allows pathological information created from a digital slide to be accessed, handled, and interpreted. Using optical pathology scanners, glass slides are collected and transformed to digitized glass slides that can be viewed on your computer monitor. Relevant support for education and the practice of human anatomy is offered by digital pathology. With the recent developments in digital pathology led to computer-aided diagnosis using machine learning approaches. So, machine learning frameworks assist physicians in diagnosing critical cases such as cancer, tumors, etc and improve patient management. With an ever growing number of choices, it can be hard to pick a better machine learning method for pathological data. Big potential attempts are made in this paper to research the full context of digital pathology with the specifics of how artificial intelligence has contributed to digital pathology. This review also analyzes various machine learning frameworks by providing as much information as possible and quantifying what the tradeoffs will be. This paper ultimately provides the improvements in the frameworks available that will be required in the near future applications.

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Attendance Management System Using Face Recognition Method

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Journal of Physics: Conference Series 2021年第1期2089卷

作者： Syed Mansoora Giribabu Sadineni Shaik Heena Kauser P.G Scholar Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India Associate Professor Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India Assistant Professor Department of Computer Science & Engineering PACE Institute of Technology and Sciences A.P India

When it comes to classroom management, the attendance check is a critical component. Time-consuming, particularly when it comes to open meetings, is checking attendance by calling names or by handing around a sign-in sheet to make it easier to commit fraud. An implementation of a real-time attendance check is described in this article in great detail facial recognition system and its outcomes. The system must be able to identify a student's face in order for it to work first snap a photograph of the pupil and save it in a database as a reference for future use. During the event, there were students may be identified by using the webcam, which captures photos of their faces auto-detects faces and selects students with names that are most likely to match, and lastly, depending on the facial recognition findings, an excel file will be updated to reflect attendance. To identify faces in webcam footage, the system uses a pre-trained Haar Cascade model. As a result, a 128-bit FaceNet has been generated by training it to minimise the triplet loss. The dimensions of the facial picture. When two facial pictures have similar encodings If the two facial pictures are from the same student or different. Use of the system as part of a class, and the outcomes have been extremely positive. There has been a poll done to find out more about There are both advantages and disadvantages to using a college attendance system.

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