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An Xception Model with Residual Attention Mechanism for Facial Occlusion Detection 8

An Xception Model with Residual Attention Mechanism for Faci...

引用

8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Rashmi Adyapady, R. Annappa, B. National Institute of Technology Department of Computer Science and Engineering Karnataka575025 India

ISBN: (纸本)9798350334012

Occlusions occur due to the presence of obstacles. It poses difficulty in localizing and detecting the facial region, resulting in substantial intra-expression variability caused by noise and outliers. Facial occlusions are one of the most common issues that exist in real-world images. Solving such issues is essential for improving face recognition. The main aim of this work is to detect the occluded face. This work proposes a modified Xception network along with a residual attention mechanism to detect occluded parts of the facial region. The recognition accuracy obtained with the proposed Xception network with residual attention (Xcep-RA) mechanism is 99.97%, 99.85%, and 98.95% using Webface-OCC, Labeled Faces in the Wild (LFW), and Real-World Masked Face Dataset (RMFD) datasets. Extensive experiments using Xcep-RA significantly achieved competitive results compared to state-of-the-art methods on Webface-OCC, LFW, and RMFD datasets. © 2023 IEEE.

关键词： Face recognition

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A Generalized Unknown Malware Classification 1

引用

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Rani, Nanda Mishra, Ayushi Kumar, Rahul Ghosh, Sarbajit Shukla, Sandeep K. Bagade, Priyanka Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Although state-of-the-art image-based malware classification models give the best performance, these models fail to consider real-world deployment challenges due to various reasons. We address three such problems through this work: limited dataset problems, imbalanced dataset problems, and lack of model generalizability. We employ a prototypical network-based few-shot learning method for a limited dataset problem and achieve 98.71% accuracy while training with only four malware samples of each class. To address the imbalanced dataset problem, we propose a class-weight technique to increase the weightage of minority classes during the training. The model performs well by improving precision and recall from 0% to close to 60% for the minority class. For the generalized model, we present a meta-learning-based approach and improve model performance from 48% to 72.06% accuracy. We report performances on five diverse datasets. The proposed solutions have the potential to set benchmark performance for their corresponding problem statements. © 2023, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Malware

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

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

引用

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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Quantum-Safe Federated Learning: Enhancing Data Privacy and Security 2

Quantum-Safe Federated Learning: Enhancing Data Privacy and ...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Kannan, E. Carmel Mary Belinda, M.J. Ravikumar, S. Alex David, S. Kannan, Sriram Vijay, K. Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Fidelity Investments Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331534950

Ensuring data security and privacy is highly pertinent in today's era marked by the dominance of collaborative machine learning. This study presents a novel framework, named"Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security,"which leverages quantum computing to strengthen federated learning models against adversarial risks and enhance privacy safeguards. Our research investigates the incorporation of quantum key distribution and entanglement protocols into federated learning, therefore establishing a quantum- resilient methodology. The findings demonstrate the model's ability to effectively resist advanced threats, hence assuring the maintenance of strong data security measures. The utilization of quantum technology in this context serves to strengthen the fundamental principles of cryptography, while simultaneously introducing a novel approach to enhancing privacy. Moreover, the study explores the potential of the framework to accommodate various datasets from different cooperating entities, while also resolving the difficulties presented by the diversity of the data. The framework's success in accommodating multiple machine learning models and applications is demonstrated by evaluations of scalability and computing efficiency. In essence, the research paper titled"Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security"represents a significant progression in the field of safeguarding collaborative machine learning. The quantum-resilient paradigm not only increases privacy but also offers a safe foundation for the future of collaborative machine learning. The issue at hand pertains to the growing apprehensions over the safeguarding of data privacy and security within the context of our technologically advanced day. © 2024 IEEE.

关键词： Federated learning

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Customized Framework for Backend Using Node JS

Customized Framework for Backend Using Node JS

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and technology, ICSEIET 2023

作者： Dubey, Aman Chauhan, Gaurav Singh Singh, Jatin Girdhar, Palak Bhagwan Parshuram Institute of Technology Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350329186

Backend frameworks are software libraries or tools that provide a set of pre-written code to perform common tasks and functions in web development. These frameworks are designed to help developers build server-side applications more efficiently and effectively. Backend frameworks provide a structure and set of conventions that help developers quickly and easily build web applications, increasing development speed. Many backend frameworks are designed with scalability in mind [1], which makes it easier to build web applications that can handle large amounts of traffic and data. Security features like protection against frequent web attacks such as SQL injection and Broken Authentication are often included in backend frameworks[2]. Backend frameworks provide a consistent structure and set of conventions, making it easier to support and update applications of web over time. Since these are generally used, there are a large number of developers who are familiar with the framework, and many resources and tutorials available online, which helps to solve any problems you may encounter while developing the application[3]. Backend frameworks are software libraries or tools that provide a set of pre-written code to perform common tasks and functions in web development. These frameworks are designed to help developers build server-side applications more efficiently and effectively. In summary, backend frameworks are an essential tool for modern web development, as they provide a set of pre-built tools and functionality that can help developers create web applications more efficiently, effectively, and securely. © 2023 IEEE.

关键词： HTTP

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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...

引用

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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Privacy-Preserving and Verifiable Decentralized Federated Learning 5

Privacy-Preserving and Verifiable Decentralized Federated Le...

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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Kumar, Sanjipogu Sandeep Joshith, Telugu Sai Lokesh, Duddupudi Daya Jahnavi, Dasari Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350313123

Federated learning is a machine learning technique that allows multiple devices to collaboratively train a machine learning model without sharing their data with a central server. The data is kept on the local device, and the model is trained on the device itself. It also reduces the risk of data breaches and enhances privacy by keeping the data local to the device. It can improve the speed and efficiency of training large-scale models by distributing the workload across multiple devices. This paper proposes a solution to the security challenges faced by the federated learning framework, which allows global model construction without sharing raw data. Privacy-preserving and verifiable decentralized federated learning (PPVD-FL) is a framework designed for secure deep learning model training. It's a decentralized federated learning framework that pre-serves privacy and ensures verification. The framework uses an efficient and verifiable cipher-based matrix multiplication algorithm along with a suite of decentralized algorithms. It maintains the confidentiality of the global model and local updates and verifies every training step. PPVD-FL can protect privacy against various inference attacks and ensure training integrity, as shown by security analysis. Real-world experiments on datasets demonstrate its accuracy and practical performance. © 2023 IEEE.

关键词： Deep learning

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Brain Disease Classification along with Age Estimation from MRI 4

Brain Disease Classification along with Age Estimation from ...

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4th International Conference for Emerging technology, INCET 2023

作者： Basil Xavier, S. Manoj, Eddula Sai Rohith, Bande Abhilash, K. Velangini Amith Reddy, G. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335750

Deep neural networks can effectively be used to predict the type of brain disease caused to the patient and to estimate the age of the patient. This can be used for identifying the age related disorders. So, we are using MobileNet a machine learning algorithm, and Res Net deep convolutional neural network, by using transfer learning. In transfer learning these models are pre trained on larger data sets. These algorithms have been used to train brain MRI scans that have been divided into three classes [1] Normal, which has not been affected by any disease, [2] Mild Cognitive Impairment, and [3] Alzheimer disease. From the categorized photographs the age of the patient is determined easily. We are going to train our model by using MOBILENET and RESNET algorithms by using the dataset of MRI images along with the accompanying disease *** using this knowledge, the model may identify patterns in the MRI data that point to particular brain illnesses and patient age. The trained model is further used to find the disease type and age of the patients. The outcomes demonstrate that the technique is capable of achieving high accuracy in tasks involving both disease categorization and age estimation. In general, the suggested techniques for categorizing brain disorders and determining age from MRI scans offer a viable answer for enhancing the effectiveness and precision of medical diagnosis and treatment planning for patients with brain diseases. By this model we can predict results in larger scale real time data in short period of time. © 2023 IEEE.

关键词： Magnetic resonance imaging

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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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Revolutionizing E-commerce with 3D Visualization: An Experimental Assessment of Behavioural Shopper Responses to Augmented Reality in Online Shopping 4

Revolutionizing E-commerce with 3D Visualization: An Experim...

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4th International Conference for Emerging technology, INCET 2023

作者： Dethe, Harshal Sujit Joy, Emmanuel Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350335750

Augmented Reality (AR) has shown great potential to improve consumer purchasing decisions and experiences. By adding more information and functionality to mobile shopping, AR enables customers to browse and test goods virtually. The objective of this paper is to explore the use of augmented reality in e-commerce to enhance customers' experiences and to analyze customer behavior and satisfaction with product purchases. The methodology used in this paper is a form of markerless AR, enabling the 3D models to be placed freely in environments for all product categories with the ability to resize, rotate, and navigate as per customer needs. The study also finds how significantly the use of AR will affect the e-commerce sector in terms of purchase decisions and return rates in the future. The novelty of this work lies in providing a new AR feature to various product categories in e-commerce stores, thereby enhancing the product experience and improving the purchase decision of the customers. © 2023 IEEE.

关键词： Augmented reality

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