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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P. Sirenjeevi V. Dhanakoti Department of Artificial intelligence and Data Science SRM Valliammai Engineering College Kattankulathur Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Intrusion detection is a crucial tool for establishing a safe and reliable Network computing environment, considering the widespread and ever-changing nature of cyber threats. Network Computing offers a substantial enhancement in cost metrics by dynamically providing IT services inside our present framework of resource, platform, and service consolidations. Given that the majority of cloud computing networks rely on delivering their services via the Internet, they are susceptible to encountering a range of security concerns. Hence, it is imperative to implement an Intrusion Detection System (IDS) in cloud settings to effectively identify novel and unfamiliar threats, alongside established signature-based attacks, with a notable level of precision. In the course of our analysis, we make the assumption that a “anomalous” event in a system or network is equivalent to a “intrusion” event, which occurs when there is a substantial deviation in one or more fundamental activities of the system or network. Several recent proposals have been put out with the objective of creating a hybrid detection mechanism that combines the benefits of signature-based detection techniques with the capability to identify unfamiliar assaults through anomalies. This study presents a novel anomaly detection system at the Network, the virtual machine level, which incorporates a hybrid algorithm. The technique combines the Naive Bayes algorithm and the SVM classification algorithm with k-Means clustering. The objective is to enhance the accuracy of the anomaly detection system. In regard to the general effectiveness in terms of detection rate and low false positive rate, the results of this study indicate that the suggested solution outperforms the traditional models.

关键词： Support vector machines Training Cloud computing Computational modeling Intrusion detection Machine learning Classification algorithms

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Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust

arXiv

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arXiv 2024年

作者： Montes, Rosana Morales, Jeovani Zermeño, Noe Duran, Jeronimo Herrera, Francisco Department of Software Engineering University of Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI Spain

Classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution. © 2024, CC BY-NC-ND.

关键词： Semantics

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Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images

arXiv

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arXiv 2024年

作者： Sundaresan, Vaanathi Dinsdale, Nicola K. Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © 2024, CC BY.

关键词： Magnetic resonance imaging

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Communication Efficient Distributed Newton Method with Fast Convergence Rates

arXiv

引用

arXiv 2023年

作者： Liu, Chengchang Chen, Lesi Luo, Luo Lui, John C.S. Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong School of Data Science Fudan University China

We propose a communication and computation efficient second-order method for distributed optimization. For each iteration, our method only requires O(d) communication complexity, where d is the problem dimension. We also provide theoretical analysis to show the proposed method has the similar convergence rate as the classical second-order optimization algorithms. Concretely, our method can find (Equation presented)-second-order stationary points for nonconvex problem by (Equation presented) iterations, where L is the Lipschitz constant of Hessian. Moreover, it enjoys a local superlinear convergence under the strongly-convex assumption. Experiments on both convex and nonconvex problems show that our proposed method performs significantly better than baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Computation of the Multivariate Gaussian Rate-Distortion-Perception Function

Computation of the Multivariate Gaussian Rate-Distortion-Per...

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IEEE International Symposium on Information Theory

作者： Giuseppe Serra Photios A. Stavrou Marios Kountouris Communication Systems Department EURECOM Sophia-Antipolis France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we propose a generic method for computing the rate-distortion-perception function (RDPF) of a multivariate Gaussian source under tensorizable distortion and perception metrics. Through the assumption of a jointly Gaussian reconstruction, we establish that the optimal solution of the RDPF belongs to the vector space spanned by the eigenvector of the source covariance matrix. Consequently, the multivariate optimization problem can be expressed as a function of the scalar Gaussian RDPFs of the source marginals, constrained by global distortion and perception levels. Utilizing this result, we devise an alternating minimization scheme based on the block nonlinear Gauss-Seidel method. This scheme solves optimally the optimization problem while identifying the optimal stage-wise distortion and perception levels. Furthermore, the associated algorithmic embodiment is provided, along with the convergence and the rate of convergence characterization. Lastly, in the regime of “perfect realism”, we provide the analytical solution for the multivariate Gaussian RDPF. We corroborate our findings with numerical simulations and draw connections to existing results.

关键词： Measurement Nonlinear distortion Numerical simulation Minimization Vectors Covariance matrices Optimization

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CLEVEREATS - Uncover the hidden secrets of your pantry with our food scanner app

CLEVEREATS - Uncover the hidden secrets of your pantry with ...

引用

Intelligent Systems and Machine Learning Conference (ISML)

作者： Peddireddy Naga Sai Raghav Bommaganti Sri Veerabhadra Vikas Seelam Anideep Kalle Bhavya Vani Ramesh Kumar Panneerselvam Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddartha Engineering College Vijayawada India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddartha Engineering College Vijayawada India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

The CleverEats application is used for the purpose of evaluation of the packaged food products that all we bought daily. The main theme of this mobile application is to know the maximum intake of the packaged food products that don’t harmful for our health. The application is used in a way that it scans the product name and then returns the ingredients presents in the food product. Along with the details about the ingredients and the chemicals used while making and packaging product. It also returns the nutrition information of that particular food product. Currently if the user wants to know about the nutrition information and maximum intake of the particular chemicals present in it. Our application minimizes the work for the user. Instead of knowing the information separately it creates a platform where all the related information about the product at one place.

关键词： Operating systems Stars Machine learning Packaging Food products Mobile applications Intelligent systems Chemicals

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SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning 27th

SecureCut: Federated Gradient Boosting Decision Trees with ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Bowen Zhang, Jian Li, Jie Wu, Chentao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Yancheng Blockchain Research Institute Yancheng China Blockchain Advanced Research Center Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai China

ISBN: (纸本)9783031781681

In response to legislation, companies are now mandated to honor the right to be forgotten by erasing user data. Consequently, it has become imperative to enable data removal in Vertical Federated Learning (VFL), where multiple parties provide private features for model training. In VFL, data removal, i.e., machine unlearning, requires removing specific features across all samples, ensuring privacy in federated learning. To address this challenge, we propose SecureCut, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both instance unlearning and feature unlearning without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to the state-of-the-art methods. To the best of our knowledge, this is the first work that investigates machine unlearning in VFL scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Specifications of Virtual Reminder System using Z

Specifications of Virtual Reminder System using Z

引用

International Conference on Emerging Technologies, ICET

作者： J aviria Naveed Irfan Ahmed Jabar Mahmood Khadija Batool Department of Computer Sciences University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Formal Methods (FM) uses mathematical notations to precisely and accurately define specifications, which helps detect errors and inconsistencies in software development. For mathematical notations, a proper mathematical framework is needed to describe and specify the behavior and structure of software systems. A few specification languages provide that framework; one is Z, which significantly enhances the quality of system specification, design, and verification. This paper introduces a formal specification for a virtual reminder system using Z schema, which signifies the importance of formal specifications that can also be used for voice assistant specifications. A virtual Reminder System (VRS) is an assistant based on speech recognition and speaker identification; a personal voice-controlled aid for the user to remind about event-based tasks that do not need some specific time to be completed but are event-based like reaching a location or any keyword/phrase spoken by the user related to some specific topic. The specified keyword/phrase can be anything, such as a person's name, place name, or phrase. Using FM reduced development time, verification efforts. and maintenance costs.

关键词： Frequency modulation Costs Event detection Personal voice assistants Software systems Specification languages Software reliability Maintenance Formal specifications Software development management

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Advancing Early Detection: CNN for Automated Blood Cancer Diagnosis and Anomaly Detection

Advancing Early Detection: CNN for Automated Blood Cancer Di...

引用

IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Raji V Anburaman Seetharaman Baskar Kasi Sakthi Govindaraju Paarkavy B Department of Computer Science and Engineering S K P Engineering College Tiruvannamalai Department of Artificial Intelligence and Data Science Sri Venkateswaraa College of Technology Chennai School of Computer Science and Engineering Galgotias University Greater Noida School of Computer Science and Engineering Galgotias University Greater Noida India Department of AI&DS Saveetha Engineering College Chennai

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

The evaluation and diagnosis of diseases related to blood cancer can be intricate and time-consuming. This complexity is compounded by the reliance on manual analyses employing techniques that consume a significant amount of time. Numerous methods have been created over the past ten years for the finding, analysis, and categorization of human blood cancer. The lack of methods or models that can automatically analyze human blood cells to detect the presence of cancer, however, is still a significant issue. The development of such a model holds the potential to enhance the early detection and prevention of these diseases, thereby facilitating more prompt medical diagnoses. In our ongoing research, we present our current endeavors in the creation of a Deeper with Convolutions Neural Network (DCNN) Learning Model capable of recognizing anomalies in blood cells. We employ a Hybrid Ensemble DCNN Learning technique for the identification of blood cancer. With accuracy rates surpassing 99%, this method is one of the most sophisticated deep learning models.

关键词： Accuracy Prevention and mitigation Neural networks Refining Cells (biology) Real-time systems Convolutional neural networks Blood Cancer Resilience

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