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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

We present a new method to estimate the rate-distortion-perception function in the perfect realism regime (PR-RDPF), for multivariate continuous sources subject to a single-letter average distortion constraint. The proposed approach is not only able to solve the specific problem but also two related problems: the entropic optimal transport (EOT) and the output-constrained rate-distortion function (OC-RDF), of which the PR-RDPF represents a special case. Using copula distributions, we show that the OC-RDF can be cast as an $I$ -projection problem on a convex set, based on which we develop a parametric solution of the optimal projection proving that its parameters can be estimated, up to an arbitrary precision, via the solution of a convex program. Subsequently, we propose an iterative scheme via gradient methods to estimate the convex program. Lastly, we characterize a Shannon lower bound (SLB) for the PR-RDPF under a mean squared error (MSE) distortion constraint. We support our theoretical findings with numerical examples by assessing the estimation performance of our iterative scheme using the PR-RDPF with the obtained SLB for various sources.

关键词： Gradient methods Rate-distortion Estimation Distortion Iterative methods

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Automated Plant Leaf Classification using Ensemble Transfer Learning in CNN model

Automated Plant Leaf Classification using Ensemble Transfer ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Yuvalatha, S. Keerthika, J. Prabhavathy, S. Banupriya, M. Priyadharshini, R. Bannari Amman Institute of Technology Department of Computer Science and Business Systems Sathyamangalam India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Electronics and Communication Engineering Coimbatore India Hindustan Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Ranganathar Institute of Engineering Department of Computer Science and Engineeeirng Coimbatore India

ISBN: (纸本)9781665453424

In the agriculture production, one of the biggest challenges is detecting the plant leaf disease in their early stages which significantly affects the farmer's earnings. In this paper various transfer learning CNN model of mobile NetV2, ResNet, InceptionV3, VGG16, Densenet are used to categories the potato leaf disease into healthy, Early blight, Late blight. Data fragmentation strategies are applied to expand the number of images for the collected dataset to train the model. The mobileNetV2 achieve the accuracy of 86.8%. In this work the ensemble transfer learning for CNN model is proposed to identify the leaf disease. For the classification, majority voting ensemble techniques are used and obtained the accuracy of 94.8%. © 2022 IEEE.

关键词： Learning systems

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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

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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Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space

Trustworthy Intrusion Detection: Confidence Estimation Using...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Ioannis Pitsiorlas George Arvanitakis Marios Kountouris Communication Systems Department EURECOM France Technology Innovation Institute Abu Dhabi UAE DaSCI Department of Computer Science and Artificial Intelligence University of Granada Spain

ISBN: (数字)9783903176652

ISBN: (纸本)9798331508722

This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.

关键词： Correlation Wireless networks Intrusion detection Estimation Network security NSL-KDD Extraterrestrial measurements Reliability Anomaly detection Optimization

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A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context 11th

A New Approach for the Analysis of Resistance to Change in t...

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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Advanced Interoperable Framework for Real-Time Predictive Analysis Leveraging Machine Learning and IoT in Smart Health Monitoring systems

Advanced Interoperable Framework for Real-Time Predictive An...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Pradeesh, D. Antony Syamala, Y. Priya, P. Sripathi, Madhavi Kaza, Srilakshmi Rohith Bala Jaswanth, B. Kg College of Arts and Science Department of Electronics and Communication Systems Coimbatore India Seshadri Rao Gudlavalleru Engineering College Department of IoT Andhra Pradesh Vijayawada India The Quaide Milleth College for Men Department of Computer Science Tamilnadu Chennai India Rao Gudlavalleru Engineering College Department of Business and Management Studies Seshadri Andhra Pradesh Vijayawada India Seshadri Rao Gudlavalleru Engineering College Department of Electronics and Communication Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350327816

As health care evolves rapidly, devices enabled by the Internet of Things, and algorithms driven by Machine Learning have surfaced as transformative elements. However, these technologies often fall short due to a lack of interoperability between existing systems, real-time predictive capabilities, and strong security measures. This paper presents a novel framework designed to overcome these challenges and optimize Internet of Things applications in health monitoring systems. Our framework has been specifically created to work seamlessly across the Internet of Things devices while monitoring a wide array of health parameters. It integrates advanced machine learning models, including Extreme Gradient Boosting, Light Gradient Boosting Machine, Categorical Boosting, Random Forest and Neural Network to improve prediction accuracy. The framework also allows real-time analysis and prediction services with real-time updates available through an Internet portal. Furthermore, we employ robust security and privacy measures to safeguard patient data. Experimental results illustrate our framework's superior performance over existing systems, demonstrating its promise for creating efficient, secure smart health monitoring systems. © 2023 IEEE.

关键词： internet of things machine learning predictive analysis smart health care

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A Comprehensive Review of Real-Time Sign Language Interpretation Using Various Machine Learning Models

A Comprehensive Review of Real-Time Sign Language Interpreta...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Maneesh J J Chokiyan Karthikeyini Suresh Subramanian Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Real-time Sign Language interpretation is among the fastest-growing fields that can potentially minimize the communication gap between deaf people and the hearing population and prepare the ground for inclusivity and accessibility throughout social, educational, and professional contexts. The review discusses the development of technologies for sign languages interpretation in real time, having the main emphasis that have to be met such as high interpretation accuracy, low latency, and computational efficiency. This review methodology, which compared machine learning and deep learning algorithms and discussed different model architectures, analyzed the trends and emerging methodologies that have shaped the current state of the field and identified significant gaps in the literature. In particular, the tendency is such that the multimodality involving hand, face, and body movement participation is becoming increasingly more effective; the pre-trained models get adapted to perform sign language tasks. Most importantly, diversity in datasets promotes model robustness across languages and dialects. A bit further, practical problems are discussed with real-time interpretation deployment on mobile devices and other resource-limited platforms. The following review is designed to point out the likely best directions for future research and development that would generalize, through the advancement of real time sign language interpretation, to become an accessible and reliable technology for everyday use.

关键词： Deep learning Sign language Adaptation models Accuracy Recurrent neural networks Translation Reviews Real-time systems Convolutional neural networks Optimization

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Mental Healthcare Chatbots: Technological Advancements, Challenges, and Opportunities

Mental Healthcare Chatbots: Technological Advancements, Chal...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Balaraman Sundarambal Amirthavarshini H Suresh Subramanian Chokiyan Karthikeyini Senthil Kumar G Kirubakaran N Department of Computer Science and Business Systems Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Communication Engineering Excel Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Mental healthcare chatbots are artificially intelligent systems that have been built with the intent of simulating human conversation and provide emotional support to one’s user. They are also widely used as tools to help individuals cope with the stress, anxiety, and other mental health challenges they have been facing, including easily accessible and affordable solutions. These chatbots use NLP and emotion recognition technology to assist users in understanding their feelings and responding accordingly. This review unpacks the latest advancements of recently developed chatbots, such as GPT-4-based models, BERT and MBERT based models, sentiment analysis, and the study of multimodal systems combining text, voice, and facial expression recognition. Also addressed are important challenges, including privacy concerns, cultural inclusivity, and clinical validation. Finally, the review identifies opportunities to improve chatbots, including supporting multi-language and primary telehealth platforms and customizing user interaction with chatbots to create more inclusive and effective mental health support worldwide.

关键词： Emotion recognition Analytical models Sentiment analysis Reviews Text recognition Telemedicine Medical services Mental health Speech recognition Chatbots

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Rule Based Maliciousness Detection in computer Network using Machine Learning in Fog Node

Rule Based Maliciousness Detection in Computer Network using...

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International Wireless communications and Mobile Computing Conference, IWCMC

作者： Farooque Hassan Kumbhar Raza Nathani Kamran Ali Memon Wessam Ali Mesbah Center for Communication Systems and Sensing King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Computer Science Department National University of Computer and Emerging Sciences Pakistan Electrical Engineering Department & Center for Communication Systems and Sensing King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The internet of things (IoT) enables heterogeneous devices to independently participate in global communications; however, it exposes the low power devices with minimum capabilities to vulnerability. This has led to the rise of attackers who leverage their attacks on low power devices to collect private data, without the user knowledge. In this paper, we look at the possibility of creating a supervised machine learning mechanism that autonomously detects packet being sent over the systems before it has reached the internet. The proposed model is trained to identify distributed denial of service (DDoS) attacks for outgoing packets, and subsequently inform and send the detected data to a monitoring node. Considering the low power devices, the proposed solution enables a rule-based system where packets can be detected with binary decisions. However, the rules and detection requires decision tree model training with appropriate datasets. Our evaluations show that the proposed mechanism can detect malicious packets without incurring additional delays in the communication by forwarding all packets to intermediate routers or fog nodes for inspection.

关键词： Training Wireless communication Computational modeling Machine learning Denial-of-service attack Data models Internet of Things

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Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classiﬁ-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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