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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Nasar, Noureen Thiruthuvanathan, Michael Moses Pauls, Alen Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335774

Brain Tumor is the development of abnormal cells in our brain. There are cancerous and noncancerous brain tumors. Because they can press against healthy brain tissue or spread there, brain tumors are harmful. The early diagnosis of brain tumors is a highly challenging assignment for radiologists. The typical size of a brain tumor doubles in just twenty-five days due to its rapid growth. If not properly cared for, the patient's survival rate typically does not exceed six months. It may quickly result in death. For the purpose of early brain tumor identification, an automatic method is necessary. In this study, an automated strategy is suggested for quickly distinguishing between malignant and non-cancerous brain images. Most of the time, it can be treated if caught during the early stages. Hence the need for more and improved brain tumor detection. The most crucial part here is image processing. The medical images obtained during the test have to be appropriately analysed. Various methods such as MobileNet, EfficientNetB7, and EfficientNetV2 have been used and their efficiency has been analysed. Here we classify the dataset containing 300 images into two. The suggested system will offer improved clinical support for the field of medicine. © 2023 IEEE.

关键词： Brain

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A novel large kernel two-branch semantic segmentation network 24

A novel large kernel two-branch semantic segmentation networ...

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4th International Conference on Signal Processing and Communication Technology, SPCT 2024

作者： Zhang, Xinrui Fu, Peng School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400710636

For semantic branching in a two-branch network structure, it is crucial to quickly improve the feeling field, in addition, the feature fusion interaction of two-branching needs to take into account the structural and multi-scale information fusion of features under different feeling fields. In this paper, in order to cope with this challenge, we propose to accumulate the receptive field in dense convolution using separable large kernel convolution through an adaptive semantic extraction module, and design a selectable large kernel attention module for the problem of mismatch of receptive field of feature information. Finally, this paper designs a multiscale feature extraction module that fuses local and global information, and uses large kernel dense separable convolution in the module with the ability to extract high-dimensional semantics serially and capture multiscale in parallel. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Secure Access Control of Cloud Data Using DNA Computing 5

Secure Access Control of Cloud Data Using DNA Computing

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Tarun Kumar, Prabhat Namasudra, Suyel National Institute of Technology Department of Computer Science and Engineering Bihar Patna India National Institute of Technology Department of Computer Science and Engineering Tripura Agartala India

ISBN: (纸本)9798350383522

With the exponential development in the usage of cloud data storage across a variety of applications, developing effective access control measures to secure sensitive user data has become critical. This research presents a unique way to use deoxyribonucleic acid (DNA) computing to improve multi-factor authentication enforcement and real-time access controls in cloud storage systems. A synthetic, engineered DNA framework is intended to reliably tie user identification characteristics to randomly produced nucleic acid credentials. After multimodal encrypted biometric authentication, the system provides these DNA access tokens. They may be validated via fast polymerase cycling to enable selective decryption and, eventually, manage allowed actions on stored cloud data based on fine-grained, attributes-based access policies. The method makes revocation happen by using programmable nucleases to quickly remove access rights from DNA structures that are waiting to be used. Rigorous cryptanalysis outperforms current access paradigms in terms of robustness to both brute force and side channel assaults. With significant developments in DNA synthesis and sequencing technologies allowing for scale deployment, bio-cryptographic access control provides a strong method for improving cloud data security via DNA computing. The system greatly increases the obstacles to unwanted data access while protecting user privacy. This study is a pioneering analysis of organizing principles in biologic data processing systems to solve key data security concerns posed by growing cloud use across commercial and public sector enterprises. © 2024 IEEE.

关键词： Access Control Cloud Data Security DNA Computing DNA Cryptography Multi-factor Authentication

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A novel method using matrix coding on-chain and sharing multimedia data for improved usability and reliability

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Multimedia Tools and Applications 2024年第40期83卷 87727-87748页

作者： Yang, Lizhu Qin, Yong School of Computer Science and Engineering Guangzhou Institute of Science and Technology Guangzhou China School of Cyberspace Security Dongguan University of Technology Dongguan China

Blockchain technology has the characteristics of non-tampering and forgery, traceability, and so on, which have good application advantages for the storage of multimedia data. So we propose a novel method using matrix coding on-chain and sharing multimedia data for improved usability and reliability. Based on matrix code, we provide the block matrix coding-based on-chain storing method and the block invertible matrix decoding-based sharing method. The method progressively converts the block to create linearly coupled coded chunks, so the blockchain only sends the chunk sets that have been encoded. Each node also maintains just the ledger relevant to its own operations, which lessens the node’s storage burden. Meanwhile, we make several replications of the chunk set by adding a replication factor to increase the feasibility of chunks. Only when all of the target nodes fail is decoding reconstruction necessary, which further enhances the read performance of the blockchain. Many experimental tests are conducted to evaluate the performance based on various parameters such as time overhead, storage overhead, compression factor, failure factor, and so on. According to theoretical analysis and experimental verification, the method offers good read performance with a high recovery success rate and minimal storage occupation, while guaranteeing the availability and dependability of the block data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Autoencoder-Based Architecture for Identification and Mitigating Phishing URL Attack in IoT Using DNN

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Journal of The Institution of Engineers (India): Series B 2023年第6期104卷 1227-1240页

作者： Gopal, S.B. Poongodi, C. Nanthiya, D. Kirubakaran, T. Kulavishnusaravanan, B. Logeshwar, D. Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal India Department of Computer Technology-UG Kongu Engineering College Perundurai India

As a new and potentially devastating form of cyberattack, ‘Phishing’ URLs pose a risk to users by impersonating legitimate websites in an effort to obtain sensitive information such as usernames, passwords, and financial details. At least two-thirds of persons were hit by these phishing assaults in 2016, according to a survey (Gopal et al. in IOP Conference Series: Materials science and engineering, vol. 1055, no. 1, p. 012072. IOP Publishing). Therefore, these attacks must be addressed carefully to prevent financial losses. In order to detect and prevent new kinds of assault, in addition to the ones already in use, a defense mechanism that can learn the possibilities of attacks is required. In the IoT (Internet of Things), everything is managed remotely via a web address (URL). So, intruders can simply gain access to the controls by stealing the URL used to access them from a distant location. To identify and prevent access to phishing sites at the network layer, we offer an approach that combines a deep neural networks (DNN) model with autoencoders. In order to train the model for feature reduction, an autoencoder sends it a dataset that includes both legitimate and phishing domains to analyze. The effectiveness of the suggested approach has been tested on a number of datasets, including Open phish, UCI, Mendeley, and PhishTank. For the multiclassification dataset, these metrics for this design are: 92.89% accuracy, 93.07% recall, 92.75% precision, and 92.21% F1-score. © 2023, The Institution of Engineers (India).

关键词： Deep neural networks

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An EETR Approach for Therapeutic Response Prediction Using Gene Expression and Drug Properties 3rd

An EETR Approach for Therapeutic Response Prediction Using G...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Selvi Rajendran, P. Jawahar, Janiel Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819726707

In recent years, the role of computational methods such as machine learning and deep learning has evolved to help better understand an individual’s response to drugs. Through advancements in the discipline of precision medicine, cancer therapies are made based on an individual's pharmacogenomics data. In this way, doctors can make informed decisions about patient outcomes and reduce treatment costs. Beyond these advancements, there is enough void to precisely predict drug response. A drug’s response to therapy is dependent on a number of factors such as the drug’s physicochemical properties, pharmacokinetics, metabolism, protein, mutation, somatic variation, environment, and much more. Studying the correlation of more of these factors and their association with the drug is one of the promising ways to bridge the gap in improving results. Extra tree regression is an ensemble-based algorithm and is less prone to overfitting than other ensemble learning algorithms particularly when low bias and reduced overfitting are desired, and computational efficiency is important. In this paper, we have applied the drug’s physicochemical properties to predict the Inhibition values using an enhanced extra tree regression (EETR) algorithm. The EETR algorithm prediction outperformed both the random forest and original ETR algorithm in terms of both MSE and R2 score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Cognitive Wellness Assessment: A Comprehensive Review of Deep Learning and Machine Learning Approaches for Mental Stress Detection in the Digital Age 14

Cognitive Wellness Assessment: A Comprehensive Review of Dee...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Panda, Madhusmita Sharma, Arun Jaiswal, Garima Indira Gandhi Delhi Technical University for Women Computer Science Engineering New Delhi India Bennett University Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350344837

Mental stress is a big problem nowadays, particularly in young people. The age group that was formerly seen as the most carefree is presently experiencing a great deal of stress. Increased stress in today's world causes numerous issues like depression, suicide, heart attacks, and *** identification is important because it can help to prevent chronic stress and its negative health consequences, improve productivity and performance, and reduce the risk of accidents and injuries. It can also increase self-awareness and improve communication and relationships. Effective stress management is essential for preserving general wellbeing and guarding against the harmful consequences of long-term stress, which can have an adverse influence on relationships, mental and physical health, and physical *** incorporating stress management techniques into daily life, individuals can effectively reduce stress levels, enhance their quality of life, and promote *** in deep learning and machine learning methods, this paper offers an extensive overview of the research on stress identification. Their development over a number of years is covered. Furthermore, the study makes recommendations for future work, with a focus on increasing accuracy and utilizing new technologies. For scholars and practitioners in the field, this paper provides insightful information about the prospective applications of deep learning and machine learning in stress identification. © 2024 IEEE.

关键词： Health risks

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Quantum Algorithm for Online Exp-concave Optimization 41

Quantum Algorithm for Online Exp-concave Optimization

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41st International Conference on Machine Learning, ICML 2024

作者： He, Jianhao Liu, Chengchang Liu, Xutong Li, Lvzhou Lui, John C.S. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

We explore whether quantum advantages can be found for the zeroth-order feedback online exp-concave optimization problem, which is also known as bandit exp-concave optimization with multi-point feedback. We present quantum online quasi-Newton methods to tackle the problem and show that there exists quantum advantages for such problems. Our method approximates the Hessian by quantum estimated inexact gradient and can achieve O(n log T) regret with O(1) queries at each round, where n is the dimension of the decision set and T is the total decision rounds. Such regret improves the optimal classical algorithm by a factor of T2/3 Copyright 2024 by the author(s)

关键词： Convex optimization

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A Comprehensive Analysis of Poisoning Attack and Defence Strategies in Machine Learning Techniques 5

A Comprehensive Analysis of Poisoning Attack and Defence Str...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Surekha, M. Sagar, Anil Kumar Khemchandani, Vineeta Sharda University Computer Science and Engineering Uttar Pradesh Greater Noida India Galgotias University Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Machine learning (ML) models have achieved remarkable success in various domains making them indispensable tools for critical applications. However, their susceptibility to adversarial attacks, particularly poisoning attacks, has raised serious concerns about their security and robustness. This review study aims to provide a complete analysis of poisoning attacks and defense strategies in ML techniques. We explore the various poisoning attack strategies, their potential impact on model performance, and examine the existing defense mechanisms to mitigate these threats. By shedding light on the latest advancements and challenges in this field, this study aims to guide researchers and practitioners towards building more secure and reliable machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning Defense strategy Poisoning attack Secure machine learning

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Neural Style Transfer: A Comparative Study 15

Neural Style Transfer: A Comparative Study

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Singh, Tripty Sharma, Mansi Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutional neural network (CNN) architectures, using pre-trained models such as VGG19, DenseNet121, ResNet50, and MobileNetV2. The methodology involves leveraging the deep features extracted by the models to balance content and style in synthesized images. We delve into the specifics of the model's layer utilization, loss function definition, and TensorFlow implementation details. Experimental results demonstrate the model's ability to produce visually compelling images that seamlessly merge content and style characteristics. Densenet121 has demonstrated superior performance compared to the other models in the evaluation. This work not only contributes to the field of NST but also offers a user-friendly TensorFlow framework, fostering further exploration and advancements in the realm of computer vision and image processing. © 2024 IEEE.

关键词： Artistic image synthesis CNN DenseNet121 image processing MobileNetV2 Neural Style Transfer ResNet50 TensorFlow VGG-19

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