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Terrain identification and land price estimation using deep learning

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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Detection and Classification of Breast Cancer Using improved Grey Wolf Algorithm

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

作者： T Senthil J Deepika R. Nithya Assistant Professor Deparment of Electronics and Communication Engineering EngineeringBannari Amman institute of TechnologySathyamangalam ErodeTamilnadu India Assistant professor.Department of Information Technology Engineering Bannari Amman institute of TechnologySathyamangalam ErodeTamilnadu India Assistant professor.Department of Computer Science Engineering Engineering Bannari Amman institute of TechnologySathyamangala ErodeTamilnadu India

Breast Cancer is the hazardous infection among young ladies and most significant reason for developing destruction rate. Physically anticipation of this affliction takes longer hours and structures are to be had in significantly less number, there's motivation to expand a mechanized forecast machine for early expectation and visualization of malignancy. The sort of considerate and threatening tumor are accomplished the use of type methodologies of device becoming acquainted with wherein the device is found from the past records and predicts the sorts of late sources of info. This paper is an overall report on the execution of models utilizing Logistic Regression, Support Vector Machine (SVM) and Gray wolf algorithm(GWO). The outcomes are assessed with the exactness, accuracy, affectability, explicitness and False Positive Rate boundaries for every calculation and are thought about. These procedures are coded in Java and executed in MATLAB, the Image handling Environment. Our investigations have demonstrated that Gray wolf is the best for prescient examination with an exactness of 92.7%.We surmise from our test results that SVM is the appropriate calculation for expectation and in general Gray wolf has performed well close to SVM.

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Epoch interrogation for skin cancer detection using convolutional neural network models

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： A Poornima M Shyamala Devi M Sumithra Mullaguri Venkata Bharath Swathi S Sathishkumar K Yogesh Shubham Santosh Upadhyay Nitesh Kumar Sah Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Assistant Professor Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Final Year B.Tech Student Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai

Skin cancer is very important notable disease and it is probable to everyone nowadays, it flourishes on the area of body where it exposed to ultraviolet rays. It leads anomalous gain in skin cells. It initiate on various parts of body like face, hand and bottoms of the feet as cautious hole or spot. The initial investigation of anomalous gain is essence to cure the disease at early stage, and it still remains a feasible challenge in the scientific improvements. From the analysis, this paper endeavour to inspect the category of disease with the following improvements. Initially, the skin dataset from ISIC machine archive is utilized for image processing. Secondly, the values of dataset images are normalized by dividing all the RGB values by 255. Thirdly, keras sequential API is used to add one layer at a time, initiating from the input. The CNN can extract the features that are useful for classifying the image, by using the kernel filter matrix. MaxPool reduce the computational cost by down-sampling the image, and the relu activation function is implemented to provide non linearity to the network. The flatten layer is utilized to remodel the final feature maps into 1D vector. CNN model provides accuracy of 94.83% with 3297 images and ResNet 50 model has attained accuracy of 90.78% due to less number of images used for classification. AlexNet model has attained accuracy of 81.8% with 1300 images and GoogleNet V3 inception has attained accuracy of 96% with 3374 images. Finally Vgg16 model has attained accuracy of 97.3% with 5636 samples.

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High Utility Mining of Streaming Itemsets in Data Streams

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Journal of Physics: Conference Series 2021年第1期1962卷

作者： Abdullah Bokir V B Narasimha Research Scholar Dept of Computer Science & Engineering UCE OU Hyd-India. Hadhramout University Mukalla-Yemen. Assistant Professor Dept of Computer Science & Engineering UCE OU Hyd-India.

The traditional models for mining frequent itemsets mainly focus on the frequency of the items listed in the respective dataset. However, market basket analysis and other domains generally prefer utility obtained from items regardless of their frequencies in the transactions. One of the main options of utility in these domains could be profit. Therefore, it is significant to extract items that generate more profit than items that occurs more frequently in the dataset. Thus, mining high utility itemset has emerged recently as a prominent research topic in the field of data mining. Many of the existing researches have been proposed for mining high utility itemset from static data. However, with the recent advanced technologies, streaming data has become a good source for data in many applications. Mining high utility itemset over data streams is a more challenging task because of the uncertainty in data streams, processing time, and many more. Although some works have been proposed for mining high utility itemset over data streams, many of these works require multiple database scans and they require long processing time. In respect to this, we proposed a single-pass fast-search model in which we introduced a utility factor known as utility stream level for tracing the utility value of itemsets from data streams. The simulation study shows that the performance of the proposed model is more significant compared with the contemporary method. The comparison has been performed based on metrics like process-completion time and utilized search space.

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Fast Hybrid Adaboost Binary Classifier For Brain Tumor Classification

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IOP Conference Series: Materials science and engineering 2021年第1期1074卷

作者： S. Jayaprada G. JayaLakshmi L. KanyaKumari Associate Professor Department of Computer Science & Engineering Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Assistant Professor Department of Information Technology VR Siddhartha Engineering College Vijayawada Andhra Pradesh India Assistant Professor Department of Information Technology Andhra Loyola Institute of Engineering and Technology Vijayawada Andhra Pradesh India

The brain tumor is a dangerous disease and its characterization is a difficult undertaking for radiologists in light of the heterogeneous idea of the tumor cells. computer-Aided tasks and its implementation of the current models with their frameworks would suffice the design metrics to recognize and relate the different tumors including brain though the process with scanning of the brain would emphasize on MRI. The models with support vector machine-nearest neighbor, naïve bayes analysis are obtained aren't enough to produce the different scenario at each set of layers and its correlation values of the performance observed. We propose a design model with fast Adaboost binary classifier, ensemble approach with fast boosting algorithm with the pre-trained model's dataset to analyze the different problem which proposes a strategy for multiple application scenarios of feature extraction to provide a classification model for different tumors in the brain to improve different performance parametric values for each trained and test sets with the prediction algorithm with each design formulation.

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