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An Approach for Securing QR code using Cryptography and Visual Cryptography 1

An Approach for Securing QR code using Cryptography and Visu...

引用

1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Bhardwaj, Cheshtaa Garg, Hitendra Shekhar, Shashi GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Quick Response code (QR code) is a 2-D matrix barcode which stores data in four different encoding modes (numeric, alphanumeric, kanji, binary). They are widely used nowadays and can be seen almost everywhere whether it is on cosmetics products, general stores, billboards and so on. It has become an important part of day to day activities. As an information sharing medium, it has become so user friendly and mobile-friendly that with just one scan through smart phones you get the information stored in it. The main intent of this work is to secure QR code from unauthorized access by allowing only those who have authorization to access it by using cryptography (by encrypting and decrypting the QR code using a key value). And further security was enhanced by applying 'k' out of 'n' visual cryptography scheme on the QR code. It creates 'n' no. of share of the QR code out of which 'k' no. of shares is required to restore it. This work will briefly explain how cryptography and visual cryptography were used to secure the QR code. The experimental results showed that there was no data loss during this process. Also, if at the time of decryption wrong key is entered then the QR code will not be generated. And also it is required to input minimum k number of generated shares of the QR code for the successful retrieval of QR code. Quality of the reconstructed QR code was also measured using PSNR and SSIM which showed that reconstructed QR code was of good quality as well as original QR code and reconstructed QR code were identical. © 2022 IEEE.

关键词： Cryptography

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Assessing Performance Evaluation of Machine Learning Algorithm for Plant Disease Detection 11

Assessing Performance Evaluation of Machine Learning Algorit...

引用

11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Agrawal, Ruchi Sharma, Dilip Kumar Yadav, Anupam GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

Plants and trees are very important in the world because they are the ultimate source of food on this planet. As humans gets affected by diseases and suffers from health issues similarly plants are caught by certain insects and get destroyed. There are many types of diseases present in the plants. To detect these diseases, patterns are required to recognize *** Learning algorithms are used for the identification of diseased and healthy leaves. The dataset for the research work is taken from Kaggle and it contains leaves of apple, tomato, strawberry etc. In India, agriculture contributes lot to the GDP so to increase theagriculture production, we have to identify or detect plant disease and use pesticides and fertilizers on time in order to increase crop production. This paper talks about the detection of plant leaf disease using Random Forest Classifier, Logistic Regression, SVM, Naïve Bayes, CART, Linear Discriminant Analysis and KNN so that proper and appropriate amount of manures and fertilizers can be given to the plants on time for the prevention of the crops to decay. © 2022 IEEE.

关键词： Fertilizers

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Identification of Missing Person Using Fusion of KNN and SVM Approach 4th

Identification of Missing Person Using Fusion of KNN and SVM...

引用

4th International Conference on Recent Innovations in Computing, ICRIC 2021

作者： Rathor, Sandeep Hasan, Afreen Omar, Ankur Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (纸本)9789811688911

Each day an enormous number of people get missing from kids to senior citizens because of some mental illness or Alzheimer’s or Dementia. Out of all, most of them are trackless. Therefore, an artificial intelligence-based technique is proposed in this paper. It helps to find missing persons and can be used by the government officials like police or the common people. If a person goes missing, relatives of that person can visit the nearest police station and provide the details of the missing person which includes a photo (picture). When any person got such a missing person then, they can capture a picture of that person and upload it in our proposed model. Now, our proposed model tries to find that match in our database using the face recognition encoding technique. If a match is found, the proposed model will inform police officers along with the current location of the missing person. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Comparative Study of Machine Learning Algorithms for Voice based Gender Identification

Comparative Study of Machine Learning Algorithms for Voice b...

引用

2022 International Conference on Edge Computing and applications, ICECAA 2022

作者： Srivastava, Swati Sharma, Himanshu Garg, Deepanshi Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Gender classification is one of the major challenges in the domain of speech analysis now-a-days. Identification of gender from acoustic properties of voice such as mean, median, and frequency, is of high importance. Machine learning is used to solve this problem because it gives promising results for classification techniques. There are several algorithms that can be used to predict the gender using acoustic properties. In this work, classifiers are evaluated using 6 different machine learning algorithms. These algorithms include K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), Support Vector Machine Using Poly kernel and Gradient Boosting (GB). For the same voice sample dataset, the accuracy, recall, precision and F1 Score are computed for all the above-mentioned classifiers. The accuracy obtained from KNN is 96.71, DT is 93.33%, Random Forest is 97.72%, SVM is 98.48%, SVM using Poly Kernel is 97.72%, Gradient Boosting is 96.96%. The predicted values of the considered evaluation metrices on test data for all above classifiers evidenced the supremacy of SVM classifier as it is best fitted model for gender identification of acoustic. SVM attained the accuracy of 98.48% which is the highest among all. © 2022 IEEE.

关键词： Support vector machines

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Diabetes Prediction using Support Vector Machine, Naive Bayes and Random Forest Machine Learning Models 6

Diabetes Prediction using Support Vector Machine, Naive Baye...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Jain, Vinod Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665482714

Nowadays, diabetes is a relatively prevalent condition. This illness affects a large number of people worldwide. Numerous renal and cardiac disorders are mostly caused by diabetes. The major factor causing high blood glucose levels is diabetes. In this study, Machine Learning (ML) algorithms are utilized to estimate the likelihood that a person would get diabetes. The primary foundation of the machine learning model is a set of data. These are statistical algorithms that be taught or trained using data with hidden patterns. This study predicts diabetes using three ML models. The experimental findings demonstrate that the Random Forest ML algorithm predicts diabetes with an accuracy of 88.14 percent. © 2022 IEEE.

关键词： Decision trees

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Breast Cancer Prediction using Standard Machine Learning Algorithms

Breast Cancer Prediction using Standard Machine Learning Alg...

引用

2022 International Conference on Edge Computing and applications, ICECAA 2022

作者： Jain, Vinod Mohan, Narendra Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Lots of women are suffering from Breast Cancer all over the world. Various data set about breast cancer is available online. Machine Learning algorithms are very useful in predicting breast cancer. In this paper six standard machine learning algorithms are applied on a data set for breast cancer prediction. These algorithms are giving better results while applied diferent data sets for different diseases. After testing these algorithms, it is observed that Logistic Regression is best with 98.5% accuracy for breast cancer prediction. © 2022 IEEE.

关键词： Forecasting

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A Study: Hate Speech and Offensive Language Detection in Textual Data by Using RNN, CNN, LSTM and BERT Model 6

A Study: Hate Speech and Offensive Language Detection in Tex...

引用

6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Kumar, Anuj Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665410359

The issue of offensive speech on social networking platforms is widespread, or like face book and twitter website is dealing with it. Several approaches for intent-based text categorization have been investigated. Each technique has advantages and disadvantages depending on the type of goal, the dimension of the facts collection, the highest reach of content, and so on. Various ways for detecting dislike and incitement to hatred have been published in the literature. The primary objective of paper is to give a approximate investigation of several method for detecting hateful content and abusive content. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), and bidirectional encoder representations from transformers are among the approaches used (BERT). The impact of class weighting methods on the efficacy of deep learning techniques was investigated. Our research shows that the or before BERT model exceed another method including both un weighted and weighted offensive prose allocation. In terms of foul language categorization, the RNN and CNN models beat every alternate methods in both un weighted and weighted cases. It was discovered that the class weighting strategy significantly improved the grading precision of all four hate speech models. © 2022 IEEE.

关键词： Long short-term memory

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Application of Machine Learning for Sentiment Analysis of Movies Using IMDB Rating 11

Application of Machine Learning for Sentiment Analysis of Mo...

引用

11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Rathor, Sandeep Prakash, Yuvraj Gla University Dept of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665480383

In today's environment, the customer behavior related to the product is important. The rapid growth of social media has made it easier to collect information about our products. Billions of customers share information about their products on social media pages. Manually managing large amounts of social media information is difficult. However, we have proposed a framework for analyzing customer sentiment using data mining and machine learning algorithms. The proposed framework consists of several steps to processes the information and try to find the good accuracy of the classification. The proposed framework is used for sentiment analysis of movie using IMDB rating. In this paper, the data preprocessing and feature extraction are done using data mining techniques. The result analysis shows that the proposed framework can be useful for better decision-making. © 2022 IEEE.

关键词： Sentiment analysis

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Plant Disease Classification Using Deep Learning Framework 1

Plant Disease Classification Using Deep Learning Framework

引用

1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Verma, Ayushi Shekhar, Shashi Garg, Hitendra GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

This Agriculture is the main source of livelihood in India. Major destruction occurs in the field of agriculture is due to the diseases in plants. Disease causes heavy devastation in the field of farming. An automatic system needs to be developed for the prevention of crops at initial stages from diseases. The automatic system will diagnose plant disease and identify its category. The paper proposed an automatic disease detection system which recognize the category of the diseases and healthiness in plants. The classification of diseases have done on the basis of symptoms appeared on the leaf surface. The paper broadly categorises the diseases into three categories such as fungal, bacterial and viral. The deep learning approach namely Convolutional Neural Network is used for the classification of the diseases. The dataset consists of 64963 number of samples in which training has performed on 80% of the samples of dataset and validation has performed on 20% of the samples of dataset. The simulation results shows that the convolutional neural network that have been trained on given dataset acquired an accuracy of 99.12%. © 2022 IEEE.

关键词： Convolutional neural networks

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Smart Automatic Accident Detection & Information System 1

Smart Automatic Accident Detection & Information System

引用

1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Rathor, Sandeep Dubey, Prateek Samadhiya, Shivam GLA University Dept of Computer Engineering Applications Mathura India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Our lives have been made easier by the advancement of technology and infrastructure. The introduction of technology has also led to an increase in traffic risks and accidents on the road, resulting in significant loss of life and property due to inadequate emergency resources. This paper consists of a few automated devices that detect accidents and notify responsible person. This technology may be used to pinpoint the location of the accident, directing medical assistance there promptly. The system is equipped with an accelerometer, GPS, and a GSM module for message transmission. Accelerometers detect abrupt changes in the magnitude, acceleration, and speed of a vehicle's axis. Additionally, a switch is provided to halt the transmission of a message in the unlikely event that there is no casualty;this can save the responsible person's valuable time. The GSM module delivers an alert message to the mobile device informing it of the accident's location. The location of the accident is indicated via a Google Maps link obtained from the GPS module's latitude and longitude. After confirming the situation, essential actions will be done, which will aid in obtaining rescue assistance in a timely manner and so saving a precious human life. In addition to detecting alcoholic drivers and fires in vehicles, this paper can also produce alerts for those situations. © 2022 IEEE.

关键词： Global system for mobile communications

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