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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Shanmugam, Thirumurugan Sivaraman, Arun Kumar College of Computing and Information Sciences University of Technology and Applied Sciences-Suhar Suhar Oman Photon Interactive Inc. Digital Engineering Solution Centre TorontoON Canada

ISBN: (纸本)9798331542573

The increase in the use of Internet of Things, otherwise IoT, has impacted numerous industries such as health, facility, and manufacturing. Although connections have become more sophisticated, security and privacy risks have also emerged through connected devices. This research focuses on the security risks posed by IoT devices and seeks to recommend measures to protect these IoT networks. Based on the analysis of the state-of-the-art, the areas that should be further secured are defined, including energy-aware security mechanisms, privacy-preserving protocols, and edge computing vulnerabilities. The results indicate that 78% of IoT devices used in healthcare facilities can be compromised to release private info to unauthorized personnel;65% of smart building systems are not secure enough when it comes to encryption. Furthermore, the research showed that by implementing artificial intelligence, organizations can decrease security threats by as much as 42 percent in IoT settings. The study then calls for a layered security solution that incorporates energy management, data protection, and the development of a comprehensive security solution for the IoT systems. Further work should be directed to the research of the flexible safety frameworks, which would allow handling new threats in terms of secure functioning of IoT networks. © 2024 IEEE.

关键词： Differential privacy

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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An Effective Analysis of Proficient Two Level Security Contraptions for Loading Data in Cloud 1

An Effective Analysis of Proficient Two Level Security Contr...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Arulkumaran, G. Jayagopalan, Santhosh Thanga Mariappan, L. Balamurugan, P. School of Computing and Information Technology REVA University Bengaluru India British Applied College Department of Computing Umm Al Quwain United Arab Emirates School of Information Technology and Engineering Vellore Institute of Technology Vellore Department of Smart Computing Vellore Dist Tamil Nadu India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Chengalpattu Dist Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350345957

One of the cornerstones of the future generation of computing is cloud computing, a form of internet-based computing. It allows for the instantaneous allocation of Internet-accessible resources whenever they are needed. Services such as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are made available to people and businesses through cloud computing. By giving customers a low-cost, scalable, and financially-independent place to store their data, cloud storage services have seen rapid revenue growth in recent years. An interoperable cloud computing system is built on open architectures and interfaces that support both private and public cloud services. Many businesses today are making the switch to the cloud because of the numerous advantages it offers in terms of data storage, retrieval, management, and availability. While cloud computing is receiving a lot of attention, it is imperative that security problems be addressed first. This study proposes a crypto-stegno mechanism in the cloud as a solution to cloud security problems. This study proposes a practical method for ensuring data privacy and security in cloud storage. In order to obtain their own public and private key, users must first register their information in the cloud and come up with a unique username and password. Cryptography, a means of concealing information and sending information in an unreadable format, is employed in the next level of security. Secure data transmission and storage on the web, electronic commerce, digital media privacy, and ATM transfer are all dependent on cryptography to some degree. Repudiation, integrity, authenticity, and confidentiality are all possible using today's cryptographic methods. Signcryption is a lightweight cryptographic system that combines encryption and signatures in a logical way to achieve higher security with less computing time. © 2023 IEEE.

关键词： Cryptography

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The Impact of CLIP Encoders and CNN Architectures on Model Efficiency: A Case Study on Hate Speech Detection 32

The Impact of CLIP Encoders and CNN Architectures on Model E...

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32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Kälin, Christof Rushe, Ellen Kull, Stefan McCarren, Andrew School of Computing Dublin City University Dublin 9 Ireland Lucerne School of Computer Science and Information Technology Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland

In this paper, the impact of model architecture, model size and overall efficiency of deep vision-language models is explored, focusing on the trade-off between performance and resource utilisation. Specifically, the effectiveness of Contrastive Language-Image Pre-training (CLIP) encoders combined with Convolutional Neural Networks (CNNs) are evaluated for this task. To this end, a study is performed using the Facebook Hateful Memes Dataset (HMD) to train and evaluate the Hate-CLIPper architecture and its variations, alongside smaller scale extentions using convolutional layers. Though the evaluation demonstrates that Hate-CLIPper shows the strongest performance, the reduced versions of Hate-CLIPper’s cross-fusion mechanism see a large decrease in parameters without a comparably large decrease in performance. This suggests that the addition of parameters to these models may ultimately lead to diminishing returns in terms of performance. We also show that downscaling a smaller version of HateCLIPper leads to a larger reduction in performance than its larger-scale counterparts, suggesting a non-linear relationship between the number of parameters and performance gains. The question of overparameterisation in Hate-CLIPper is therefore raised, highlighting the importance of balancing model complexity with training efficiency. Through this case study, this research contributes to the development of more efficient methods for automatic hateful meme detection which, in extension, can improve content moderation practices and reduce the spread of online hate speech. © 2024 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes 27

TransFeat-TPP: An Interpretable Deep Covariate Temporal Poin...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Meng, Zizhuo Li, Boyu Fan, Xuhui Li, Zhidong Wang, Yang Chen, Fang Zhou, Feng Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

ISBN: (纸本)9781643685489

The classical temporal point process (TPP) constructs an intensity function by taking the occurrence times into account. Nevertheless, occurrence time may not be the only relevant factor, other contextual data, termed covariates, may also impact the event evolution. Incorporating such covariates into the model is beneficial, while distinguishing their relevance to the event dynamics is of great practical significance. In this work, we propose a Transformer-based covariate temporal point process (TransFeat-TPP) model to improve the interpretability of deep covariate-TPPs while maintaining powerful expressiveness. TransFeat-TPP can effectively model complex relationships between events and covariates, and provide enhanced interpretability by discerning the importance of various covariates. Experimental results on synthetic and real datasets demonstrate improved prediction accuracy and consistently interpretable feature importance when compared to existing deep covariate-TPPs. Our code is available at https://***/waystogetthere/***. © 2024 The Authors.

关键词： Spatio-temporal data

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FEATURE-pHLA: Physico-chemical features efficiently predict peptide-HLA binding affinity

FEATURE-pHLA: Physico-chemical features efficiently predict ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Alhosani, Hamda Mall, Raghvendra Singh, Ankita Castiglione, Filippo Technology Innovation Institute Biotechnology Research Center Abu Dhabi United Arab Emirates National Research Council of Italy Institute for Applied Computing Rome00185 Italy

ISBN: (纸本)9798350386226

Human leukocyte antigen or HLA plays a crucial role in the recognition of antigenic peptides as this binding is responsible for subsequent immune response by eliciting T-cell activation. Accurate prediction of peptide-HLA binding affinity is imperative for facilitating vaccine development and immunotherapies. Recent advancements in transformer-based models and protein language models in predicting peptide-HLA interactions have shown significant improvements. Current methodologies rely on deep learning methods and GPU-intensive computations. We propose a simple and computationally cheaper method that demonstrates efficacy. Our tree-based model, named FEATUREPHLA, utilizes the physico-chemical fingerprints obtained from peptides and HLA sequences and is highly *** goal was to estimate the predictive efficacy of these physico-chemical features for the task of peptide-HLA binding prediction. Our proposed method outperforms other methods on experimentally verified peptide-HLA binders from the HPV vaccine data securing the highest number of true positives and the lowest number of false negatives, thereby, showcasing its predictive power on real-world scenarios. Our study reveals the relevance of biology-inspired features for the calculation of molecular interactions and lays the groundwork towards developing more accurate biology-informed predictive models. © 2024 IEEE.

关键词： Vaccines

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A Prototype for Machine Learning Model Deployment in Cloud Environment

A Prototype for Machine Learning Model Deployment in Cloud E...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Ramalakshmi, K. Agarwal, Aditya Gunasekaran, Hemalatha Khan, Sameer Ali Mandal, Nilesh Kumar Alliance University Computer Science & Engineering Bengaluru India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman

ISBN: (纸本)9798350317060

Training and developing a Machine Learning (ML) model is a difficult task, and then after successfully creating a working model, deploying and distribution is an added feature. In most instances, those models are never deployed. To help with this, we present a prototype, a SaaS platform to allow users to dynamically deploy their machine learning models to the cloud and host them so that the user has complete control over the visibility and *** delivery and deployment model provides lower upfront cost, timely updates, and a dedicated work/host environment. The platform's sole purpose revolves around the idea of a sharable deployable and ready to use Machine Learning Model. It takes advantage of the Continuous Integration and Continuous Delivery archetype facilitated by Kubernetes to dynamically provide custom and updated with the latest libraries docker environment. © 2023 IEEE.

关键词： Machine learning

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NONLINEAR RECONSTRUCTION FOR OPERATOR LEARNING OF PDES WITH DISCONTINUITIES 11

NONLINEAR RECONSTRUCTION FOR OPERATOR LEARNING OF PDES WITH ...

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11th International Conference on Learning Representations, ICLR 2023

作者： Lanthaler, Samuel Molinaro, Roberto Hadorn, Patrik Mishra, Siddhartha Computing and Mathematical Science California Institute of Technology PasadenaCA United States Seminar for Applied Mathematics ETH Zurich Zurich Switzerland

A large class of hyperbolic and advection-dominated PDEs can have solutions with discontinuities. This paper investigates, both theoretically and empirically, the operator learning of PDEs with discontinuous solutions. We rigorously prove, in terms of lower approximation bounds, that methods which entail a linear reconstruction step (e.g. DeepONet or PCA-Net) fail to efficiently approximate the solution operator of such PDEs. In contrast, we show that certain methods employing a nonlinear reconstruction mechanism can overcome these fundamental lower bounds and approximate the underlying operator efficiently. The latter class includes Fourier Neural Operators and a novel extension of DeepONet termed shift-DeepONet. Our theoretical findings are confirmed by empirical results for advection equation, inviscid Burgers' equation and compressible Euler equations of aerodynamics. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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A generalized software reliability prediction model for module based software incorporating testing effort with cost model

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Life Cycle Reliability and Safety Engineering 2024年第4期13卷 419-436页

作者： Yadav, Akshay Kumar Srivastava, Shilpa Pant, Millie Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee 247001 India Christ University Karnataka Bengaluru 560029 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee 247667 India

As software innovation has advanced, it has been noted that the testing effort function (TEF) is one of the key factors influencing the improvement of software reliability. This paper presents a simplified model which incorporates the testing effort for the reliability growth of a software. A closed form solution has been derived for the reliability of the software. This study examines the impact of testing efforts on a software reliability model based on NHPP. The sensitivity analysis has been made available to investigate how the created model's system parameters affect the cost function, mean value function, and software’s reliability. The parameters of the model have been estimated using the non-linear least square estimation (MLE) method in MATLAB software. Additionally, a warranty cost model is constructed to assess the optimal release policy for the software. The general form of the reliability expression involves elliptic integrals, which can be computed easily through a software like Mathematica. We have derived analytical solutions for reliability pertaining to several particular cases. Optimal release time for the software product has been calculated for some particular cost-sets. Goodness of fit curves have been plotted to compare the proposed model with some well-known existing SRGMs. Numerical illustrations are provided to bolster the analytical outcomes. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.

关键词： Maximum likelihood estimation (MLE) Non-homogeneous Poisson process (NHPP) Optimal release time (ORT) Software reliability growth model (SRGM) Testing effort function (TEF) Warranty cost model

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An overview of common emotion models in computer systems 45

An overview of common emotion models in computer systems

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45th Jubilee International Convention on Information, Communication and Electronic technology, MIPRO 2022

作者： Horvat, M. Stojanovic, A. Kovacevic, Z. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Zagreb University of Applied Sciences Department of Computer Science and Information Technology Zagreb Croatia

ISBN: (纸本)9789532331035

Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence and other fields of science that require data about emotions if they could be adequately described by computer systems. Today there are many different theories of affect, but few of them are used in affective computing. Other areas of computing also benefit from structured and expressive data models of the affective domain, such as human-computer interaction and brain-computer interfaces. Typical tasks include automated recognition and analysis of emotional states, mental fatigue, individual motivation, vigilance and stress resilience. In this paper four often used models of emotion and cognitive behavior are listed and their properties explained: discrete, dimensional, appraisal and action tendency models. For each model, algorithms are provided for similarity measures that can be used to determine the relatedness between different stimulation and estimation artefacts in their respective emotion spaces. The goal of this article is to help professionals find the optimal emotion model for their research and quickly become familiar with data modelling of affective states. © 2022 Croatian Society MIPRO.

关键词： Brain computer interface

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