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Future of E-commerce by Implementing Blockchain Payments System 1st

Future of E-commerce by Implementing Blockchain Payments Sys...

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1st International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2022

作者： Nagarajan, G. Jain, Naman Rathore, R. Naman Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9783031276217

Blockchain technology is presently widening into different sections of the Information technology, Banking and Finance, Currency, Healthcare, Records of Property, Voting, Communications technology community and especially the main field which plays an important part in E-commerce. Our proposed idea provides a solution to 4 new problems: faster processing of funds, ensuring user privacy and user control, providing smooth flow of various different modules and optimized database modules to manage and easily process the data to the front-end. It also keeps all the records of what is shared with whom when and why without involving any 3rd party. When paired with Data encryption keys, blockchain offers transparent, tamper-proof, and secure platforms that can allow creative solutions. In this paper we will show how e-commerce can be enabled using blockchain with all the drawbacks that a customer faces while ordering online currently. This helps the small and medium enterprise businesses to run with the confidentiality that no data or any private details will be retrieved or leaked. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Damaged Image Repair using Masks with computer Vision Inpaint Method 2

Damaged Image Repair using Masks with Computer Vision Inpain...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ramakrishna, Doddi Purna Gana Emmanuel, G. S. Michael Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision. © 2023 IEEE.

关键词： computer vision

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Faster R-CNN for Detection and Classification of Banana Leaf Diseases 1

Faster R-CNN for Detection and Classification of Banana Leaf...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Bharathi Raja, N. Selvi Rajendran, P. Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350342796

Plant diseases are important factors in reducing the appearance of agricultural commodities. Consequently, it is essential to recognize and diagnose these disorders as early as possible. To this end, the researchers suggest a Deep Learning (DL) technique to categorize diseases of the banana leaf that have been fully automated. In this paper, a transfer learning strategy reorientation of a Faster Region-based Convolutional Neural Network (Faster R-CNN) is proposed to develop an uncertainty detection. Using images acquired from different parts of the Banana plant, six distinct designs were built from 18 different classes. Early results show that the proposed method works even in challenging situations such as poor lighting, a complex environment, and variable resolution, shape, position, and orientation of real-world images. The effectiveness of multiple automated uncertainty disease detection systems is also measured through improvement and verification measures. The proposed FR-CNN model uses a pre-formed disease detection algorithm to create a network that could produce correct estimates through Deep Transfer Learning (DTL). The developed model performed better in terms of accuracy and needed less training time when compared to other deep-learning models. © 2023 IEEE.

关键词： Accuracy Agriculture Deep Transfer Learning (DTL) Faster Region-based Convolutional Neural Network (Faster R-CNN) Plant Disease Detection

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ExpACVO-Exponential Anti Corona Virus Optimization Enabled Hybrid Deep learning for Tongue Image Segmentation Towards Diabetes Mellitus Detection Via CNN-LSTM Network 1

ExpACVO-Exponential Anti Corona Virus Optimization Enabled H...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Mathew, Jimsha K Sathyalakshmi, S. Hindustan Institute of Technology & Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350330915

The surprising headways in biotechnology and public medical services foundations have prompted a pivotal creation of basic and touchy medical care information. By applying canny information examination methods, many fascinating examples are distinguished for the early and beginning discovery and avoidance of a few deadly infections. Diabetes Mellitus (DM) is a very dangerous illness since it adds to other deadly infections, i.e., heart, kidney, and nerve harm. The following stages have been proposed in this paper for the order, beginning phase ID, and expectation of diabetes: a) Data collection from tongue dataset of many images, b) Preprocessing using the gaussian filter for noise removal, c) Segmentation using U-Net-CRFRNN for segmenting region of interest and finally d) classification using the integration of Exponential Weighted Moving Average (EWMA) and Anti Corona Virus Optimization (ACVO) and also to increase the efficiency of the network, use of CNN-LSTM. The suggested architecture is more effective than other state-of-the-art models on many different parameters, based on the outcomes (accuracy: 0.94, sensitivity: 0.96, specificity: 0.96) © 2023 IEEE.

关键词： Anti Corona Virus Optimization Classification CNN-LSTM Deep learning Diabetes Mellitus Exponential Weighted Moving Average Tongue Segmentation

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Gui that Detects Liver Disease Using Machine Learning

Gui that Detects Liver Disease Using Machine Learning

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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Ashok Kumar, K. Sri Rama Subrahmanyam, K. Srinivasulu Reddy, M. Sathyabama Institute of Scienceand Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

The liver is an important organ because it produces vitamins & substances necessary for the survival of the decaying body, including iron, toxins, proteins, & carbs. It aids in vitamin and mineral metabolism, tubular muscle growth, and blood coagulation. Factors including alcohol intake, opioid painkiller usage, poor nutrition, and underlying medical disorders have all been linked to the development of liver disease. The results of a patient's blood tests and physical exam may help physicians determine risk factors for liver disease. Data mining methods may help speed up the process of identifying liver illness. When more information is utilized, the vaccination becomes more precise. This design aims to address this issue by incorporating a method of device mastery into the process of evaluating liver disease. The primary motive of this system is to apply a wide angles set of concepts to perceive healthy folks that are honestly affected by liver disease. The design of this system also aims to evaluate and contrast different bracketing algorithms according to their operational characteristics. A Python-based graphical interface for Stoner is now being development to aid clinicians in the assessment of liver issues. The user interface may be used by soothsayers and medical interpreters as a web application for liver trials. © 2023 IEEE.

关键词： Blood

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Brain Tumor Detection Using Convolutional Neural Network 5

Brain Tumor Detection Using Convolutional Neural Network

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Uke, Shailaja Kaulwar, Mayuresh Kawtikwar, Yash Khadse, Samyak Khandare, Anurag Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350338287

Tumor is the abnormal development of cells in the body. The fairly significant overgrowth of brain cells is known as a brain tumor and classification manually takes time and is only possible at a few diagnostic facilities. Therefore, it is necessary to create a system that can identify the type of brain tumor based solely on the input MR images. To evaluate these malignancies, a common imaging approach is magnetic resonance imaging (MRI), however, manual segmentation cannot be done in a timely manner due to the volume of data generated by MRI, restricting the application of exact quantitative measurements in clinical practice. This study presents a novel hybrid methodology for the classification of brain cancers based on the analysis of magnetic resonance imaging (MRI) data. The methodology compares the performance of different classifiers, namely Convolutional Neural Networks (CNNs), K-nearest neighbor, Support Vector Machine and Logistic Regression in predicting the presence of brain tumors. The CNN model is specifically employed to get features from the MRI data and apply them to the classifiers. The results of the study show that the different CNN models, along with KNN, SVM, LR, and Long Short-Term Memory Networks were taken for tumor classification. The achieved accuracies were 82.35%, 78.43%, 61.26% and 74.28% respectively. Among these algorithms, KNN demonstrated the highest accuracy, indicating its effectiveness in accurately classifying brain tumors based on the MRI data. By integrating these classifiers and utilizing the CNN model for feature extraction, this hybrid methodology presents a promising method for classifying brain tumors from MRI data. The findings highlight the potential of combining deep learning techniques like CNNs with traditional machine learning algorithms to increase the reliability of diagnosing brain tumors and contribute to the field of medical image analysis. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation 3

Antarlekhaka: A Comprehensive Tool for Multi-task Natural La...

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3rd Workshop for Natural Language Processing Open Source Software, NLP-OSS 2023

作者： Terdalkar, Hrishikesh Bhattacharya, Arnab Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9798891760455

One of the primary obstacles in the advancement of Natural Language Processing (NLP) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP. The tool is Unicode-compatible, language-agnostic, Web-deployable and supports distributed annotation by multiple simultaneous annotators. The system sports user-friendly interfaces for 8 categories of annotation tasks. These, in turn, enable the annotation of a considerably larger set of NLP tasks. The task categories include two linguistic tasks not handled by any other tool, namely, sentence boundary detection and deciding canonical word order, which are important tasks for text that is in the form of poetry. We propose the idea of sequential annotation based on small text units, where an annotator performs several tasks related to a single text unit before proceeding to the next unit. The research applications of the proposed mode of multi-task annotation are also discussed. Antarlekhaka outperforms other annotation tools in objective evaluation. It has been also used for two real-life annotation tasks on two different languages, namely, Sanskrit and Bengali. The tool is available at https://***/Antarlekhaka/code. © 2023 Association for Computational Linguistics.

关键词： Natural language processing systems

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Web Application Security Testing Framework using Flask 2

Web Application Security Testing Framework using Flask

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Rohit, R. Hasan Firnas, I. Abishek, M. Albert Mayan, J. Dhamodaran, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

This research presents a novel framework for automated web application security scanning and information gathering using the Axiom methodology. The framework assists organizations and security researchers in identifying and mitigating vulnerabilities in web applications by automating the discovery of publicly available assets and filtering targets based on initial responses, open ports and other criteria. The use of the Axiom methodology allows for faster scanning times and distributed scanning, making it useful for bug bounties and penetration testing. The framework's performance, limitations and challenges are evaluated. The research demonstrates the potential of the framework in improving the efficiency and scalability of web application security while emphasizing the need for proper configuration and ethical considerations. © 2023 IEEE.

关键词： Philosophical aspects

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SDNS: Artificial Neural Network Scheme to Solve the Nonlinear Skin Disease Model 2

SDNS: Artificial Neural Network Scheme to Solve the Nonlinea...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Ebenezer, V. Sachin, R. Hiruthic, S.S. Sergius, S. Sam Shivnesh, R. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350346640

In the area of medical diagnosis, there has been a need for accurate anomaly detection and disease classification. Data-driven diagnosis is necessary since skin disorders tend to take many different forms, there aren't enough skilled dermatologists, and they're scattered inaccurately. It is important to diagnose and treat skin diseases promptly, but some conditions may not present symptoms right away, which can complicate treatment for certain patients. The patients are also concerned about the treatment and diagnosis. This makes patients a lot harder to reach out to doctors in-order for diagnosis thus an automation is required which gives the results based upon pictures taken personally. © 2023 IEEE.

关键词： Neural networks

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Self-Attention Vision Transformer with Transfer Learning for Efficient Crops and Weeds Classification 6

Self-Attention Vision Transformer with Transfer Learning for...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Sharma, Shubham Vardhan, Manu National Institute of Technology Department of Computer Science and Engineering Raipur492010 India

ISBN: (纸本)9798350346961

Classifying and mapping weeds are crucial for ensuring that plantations remain free of unwanted plants, particularly in cases where plant spacing is uneven, making weed detection more challenging than it is for crops. To address this issue, Deep Neural Networks (DNNs) are often used in agriculture to identify the unique features of weeds and classify them based on low-resolution imaging, which helps to control weed populations. Convolutional Neural Networks (CNN) in Deep Learning (DL) models have advanced, achieving remarkable performances in plant-weed classification. Despite Convolutional Neural Networks (CNNs) success in computer vision, they still need help with massive labeled datasets, intra-class classification, and high processing costs when used in classification tasks. Meanwhile, transformer-based models have shown significant modeling capabilities globally but are yet to be thoroughly investigated in agriculture. In this study, we explore Self-Attention Vision Transformers (ViT) using a Transfer Learning (TL) approach to classify weeds in benchmark datasets. The proposed model, ViT-S16, was fine-tuned on the ImageNet-1k dataset, pretrained on the ImageNet-21k, and evaluated on the public benchmark agriculture dataset DeepWeed, which contains plants grown with various weed species under different conditions. Our experimental results show that ViT-S16 outperforms state-of-the-art CNN models ResNeXt-50 and EfficientNet-B7, achieving an utmost accuracy of 98.54%. These results show how helpful ViT can be in different image classification tasks. In the future, researchers can look into the strengths and weaknesses of various ViT models.. © 2023 IEEE.

关键词： Convolutional neural networks

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