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Detection and Classification of Breast Cancer Using improved Grey Wolf Algorithm

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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Transfer Learning approach for grading of Diabetic Retinopathy

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

作者： T Aswathi T R Swapna S Padmavathi Student Research Scholar Department of Computer Science Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India. Assistant Professor Department of Computer Science Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India.

There has been a wide interest in applying Deep Learning (DL) algorithms for automated binary and multi class classification of colour fundus images affected with Diabetic Retinopathy (DR). These algorithms have shown high sensitivity and specificity for detecting DR in non-clinical setup. Transfer learning has been successfully tested in many medical imaging applications like skin cancer detection, pulmonary nodule detection, Alzheimer's disease etc. This paper experiments with the different DL architectures such as VGG19, InceptionV3, ResNet50, MobileNet and NASNet for automated DR classification (binary and multi class) on Messidor dataset. The dataset is publicly available, and comprises of 1200 retinal fundus images. The images belong to four different classes of DR namely, normal (class 0), mild (class 1), moderate (class 2) and severe (class 3), graded based on the severity level of DR. In our experiment, we have enhanced the quality of input images by applying algorithms like CLAHE (Contrast Limited Adaptive Histogram Equalisation) algorithm and Powerlaw transformation as pre-processing techniques, which work on the small image patches with high accuracy, contrast limiting and image sharpening. Hyper parameter tuning on pretrained InceptionV3 architecture, resulted in enhancing the accuracy of the model. Both binary and multi class results were analysed considering inter class (one class with another class) accuracies. We achieved an accuracy of 78% between class 0 and class 1, the accuracy between class 0 and class 2 further reduced to 69%, while class 1 and class 2 showed an accuracy of 61%. Moreover, the interclass class accuracy between class 1 and class 3 was 62%, class 2 and class 3 further reduced to 49%. The accuracy further diminished between class 0 and class 3 to 32%. These experiments suggest that the pretrained models provided better results in classifying normal and mild, but they were not that much efficient in classifying moderate-severe and

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IOT Based Tool Garbage Management System

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

作者： S Sathyabama A Bharath Raaj S S Cibi Saamraat T Girimurugan Assistant Professor Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India UG Students Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India

These days, safe and hygiene disposal of waste via dustbins in smart cities needs a centralized and monitored system. This system monitors various bins used for rubbish and garbage collectionextent is also informed inside these garbage bins via centralized online Application. For this the system the bins are fitted specially placed ultrasonic sensors to determine the level and depth of rubbish. It incorporates an AVR based microcontroller, GSM Module, Wi-Fi modem, a buzzer for alert and LCD screen for viewing data. The voltage support is given by a 12V transformer. To determine the display status and the extent to which the rubbish bin is loaded an LCD screen is used. These LCD screens are used inside the rubbish bins. To point out the condition however the web application is designed which is used to monitor the garbage or rubbish bins. The geographical view of rubbish bins can be obtained by the web page and also these web pages indicate the colored rubbish that they can highlight

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Secured Communication in Vehicular Adhoc Networks (VANETs) using Blockchain

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

作者： Shivaprasad More Rahul Sonkamble Udaykumar Naik Shraddha Phansalkar Priyanka More Baljit Singh Saini Assistant Professor Department of Computer Science and Engineering Sanjay Ghodawat University Kolhapur Maharashtra India Professor Department of Electronics & Communication KLE Dr. M.S. Sheshgiri College of Engineering & Technology Belagavi Karnataka India Professor Symbiosis Institute of Technology Lavale Pune Research Scholar School of Computer Science and Engineering Lovely Professional University Punjab Professor School of Computer Science and Engineering Lovely Professional University Punjab

In the last decade, the vehicular ad-hoc network (VANET) field is growing drastically. In VANET the communication is the most important aspect through which the data transmission is taking place. Data transmission is maybe related to data in the form of multimedia messages, notifications, announcements, or some warning messages that are involved in VANETs. Data transmission among different vehicles would lead to an exchange of audio-video files in the formed network. These multimedia messages should be transmitted in the fraction of second as this network is built for temporary communication. Vehicles involved in this communication should be trustworthy otherwise other vehicles those who are part of this network would be misguided due to intruder present in the network. To achieve security and integrity in the constituted network, system has proposed the blockchain security environment as a part of this proposed system. Blockchain induces high-end communication in the ad-hoc network. It also enhances the overall security aspect of the constituted network. In this paper, we have proposed blockchain-based security system for vehicular communication that handles this communication securely in a well efficient manner. The performance of the proposed system will be measured based on parameters like end-to-end delay, reliability, and packet delivery ratio.

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(CSM2-RA-R2-TI): Cyber Security Maturity Model for Risk Assessment Using Risk Register for Threat Intelligence

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Journal of Physics: Conference Series 2021年第1期2040卷

作者： B Lakshmi Prasanna M SaidiReddy Research Scholar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India. Assistant Professor Department of Computer Science RBVRR Women's College Hyderabad Telangana India. Associate Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India.

Because of the growing frequency sophistication, and severity of cyber security attacks, all businesses should ensure that cyber security risk is properly addressed in their enterprise risk management (ERM) programs. Risk Management, agreeing with the NIST Guide 8286 is the arrangement of 'composed exercises to direct and control an association concerning risk'. NISTIR 8286 characterizes a system and framework for hazard the executives. In any case, executing this norm without an in-depth plan can turn into a risk on associations. This paper provides a capability maturity model for risk assessment for threat intelligence using a risk register. This model helps the organization as reference and set clear path to survey risk assessments in accordance with latest threats.

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Full Fledged Automated Human Resource Management Software Suite

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

作者： M Sri Sivasankaran T Suganya UG Student Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India. Assistant Professor Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India.

The Human Resource Management System is essentially worried about dealing with the Administrator of HUMAN RESOURCE Department in an organization. A Human Resource Management System alludes to the frameworks and cycles at the crossing point between human asset the executives and data innovation. It blends HRM as an order and specifically its essential HR exercises and cycles with the data innovation field, though the programming of information preparing frameworks developed into full-fledged HR SUITE. The fundamental target of this paper is to diminish the exertion of Administrator to keep the day-by-day occasions, for example, participation, projects, works, arrangements, and so forth This paper manages the way toward distinguishing the representatives, recording their participation hourly and ascertaining their successful payable hours or days. This paper FULL fledged HR management suite ought to keep up the records of every single worker and their time spend in to organization, which can be utilized for execution evaluation. In view of that move, expulsion, advancement should be possible.

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A reversible data hiding method for image protection

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AIP Conference Proceedings 2021年第1期2387卷

作者： S. S. Saranya C. Santhosh M. Santhiya S. Jeevana J. Dhivya A. Vanitha 1Assistant Professor Computer Tehnology UG Kongu Engineering College. Perundurai Tamil Nadu-638060 India 2Assistant Professor Department of Computer Science Engineering Nandha Engineering College Perundurai Tamil Nadu-638052 India 3UG Scholar Computer Tehnology UG Kongu Engineering College Perundurai Tamil Nadu-638060 India

Encryption is the way toward encoding given information that can't be perceived by an unapproved individual. Securing a message or a picture which is partaken in a social stage be the difficult undertaking these days. This paper proposes an account procedure to steganography by means of reversible room before encryption surface mix. Reversible room before encryption surface mix measure explore supplementary unobtrusive side picture that arranges different facet picture with a similar and relative appearance with optional dimensions. This paper networks the Reversible room before encryption surface association cycle with steganography for covering secrete data. Rather than using contemporary cover photo for covering information our figure seal the source surface picture and introduces coded message via pattern of Reversible room before encryption surface association. This licenses us to isolate the secret messages and source surface from a stego designed surface. This philosophy proffers main three focal points. Most importantly, the positioning provide data embedding furthest extended that is contrast to dimensions of surface of the stego picture. Second, a steganalytic compute not apparently to conquer steganography technique. Third, changeable capacity gained from our arrangement gives value, which grants recovery surface of the source. Exploratory results are guaranteed to provide various consignment of introducing curtailment, produce ostensibly viable surface pictures, and also recuperate fount surface.

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