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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Moondra, Amit Chahal, Poonam Manav Rachna International Institute Of Research And Studies Department Of Computer Science Engineering Haryana Faridabad India

ISBN: (纸本)9798350330861

The human brain possesses millions of neurons that enable easy identification of different speaker voices. However, when a speaker's voice is accompanied by significant background noise, recognizing the speaker becomes challenging. In our experiment, we are focusing on an efficient approach for extracting features that support speaker recognition even with degraded human voice. Mel-Frequency Cepstral Coefficient (MFCC) is a commonly used method for extracting human speech features and is widely utilized in speaker recognition systems. The process of extracting features from MFCC involves dividing the audio into frames and calculating cepstral coefficients for each frame. Each frame in the MFCC exhibits similar spectral power, representing the distinct and shared vocal characteristics of each speaker. The MFCC provides numerical values for these vocal properties, which are effectively employed in identifying the speaker, whether through a Machine Learning (ML) or Artificial Intelligence (AI) enabled system for speaker identification. In this article, we employ a Gaussian Mixture Model (GMM) for speaker classification in scenarios where there is high background noise. Additionally, the experiment investigates the impact of sampling rate on human voice in situations where the signal-to-noise ratio (SNR) is low throughout the speaker recognition process © 2023 IEEE.

关键词： Feature extraction

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Optimizing Causal Interventions in Hybrid Bayesian Networks: A Discretization, Knowledge Compilation, and Heuristic Optimization Approach 20th

Optimizing Causal Interventions in Hybrid Bayesian Networks...

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

作者： Vonk, Maarten C. Vermetten, Diederick de Nobel, Jacob Brand, Sebastiaan Malekovic, Ninoslav Bäck, Thomas Laarman, Alfons Kononova, Anna V. Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands The Hague Centre for Strategic Studies The Hague Netherlands

ISBN: (纸本)9783031740022

Causality is increasingly integrated into decision-making processes. Often, the goal is to optimize over causal interventions to achieve specific policy objectives. However, research into causal optimization has bifurcated into either the online optimization of interventions in causal models or the offline optimization of decision rules in causal influence diagrams. This paper introduces an approximate method for offline optimizing interventions in arbitrary hybrid Bayesian networks using observational data. The optimization problem is approached by compiling discretized Bayesian networks as binary decision diagrams, whereafter running interventional queries is very efficient. This efficiency is exploited by running heuristic optimization algorithms to optimize over the interventional queries. By running experiments on a variety of large hybrid Bayesian networks, we demonstrate the practical utility of our method and discuss policy relevance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Bayesian Networks Causal Inference Do-operator Heuristic Optimization Knowledge Compilation Optimal Causal Interventions

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Machine Learning based Anomaly and Threat Detection System in Real Time Social Internet of Things using Dimensionality Reduction Techniques

Machine Learning based Anomaly and Threat Detection System i...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Anciline Jenifer J Preethika S.K. Piramu Department of Computer Science Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Social Internet-of-Things (SIoT) is an innovative model developing in the struggle to safeguard privacy and increase trust inside the Internet-of-Things(IoT). The revolutionary notion of SIoT combines the IoT with social platforms, allowing inanimate items to develop relationships with one another. The fast growth of SIoT has transformed networked networks, enabling effortless interaction between devices. However, integrating these systems creates major security issues, including anomalies and attacks that may compromise system integrity. This study presents a machine learning(ML) methods like Isolation Forest (IF), One class support vector Machine(SVM) and Kernel density estimation(KDE) for in SIoT networks using the Principal component Analysis(PCA) and Correlation Analysis as dimensionality reduction techniques. The findings shows that the IF method provides an accuracy of 93% with the dimensionality reduction techniques applied on RT-IoT dataset. By utilizing powerful machine learning algorithms and contextual analytics, the framework improves detection accuracy and enables strong device collaboration in highly dynamic contexts..

关键词： Dimensionality reduction Performance evaluation Correlation Machine learning algorithms Real-time systems Vectors Threat assessment Social Internet of Things Security Kernel

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LightGBM empowered by whale optimization for thyroid disease detection

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International Journal of Information Technology (Singapore) 2023年第4期15卷 2053-2062页

作者： Sinha, Bam Bahadur Ahsan, Mohammad Dhanalakshmi, R. Department of Computer Science Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi 834010 India Informatics Cluster School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Department of Computer Science Engineering Indian Institute of Information Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620009 India

Thyroid disorder is a significant source of formulation in medical classification and prognosis, with onset being a challenging assumption in medical study. The thyroid gland is a vital organ of our body. Thyroid hormone releases are responsible for metabolic regulation. Hyperthyroidism and hypothyroidism are two of the most prevalent thyroid disorders that affect the thyroid’s ability to regulate the pace of the body’s metabolism. Data cleansing procedures were used to prepare the data to the point where analytics could be performed to determine the risk of individuals developing thyroid disease. In this paper, an optimal framework design employing LightGBM (Light Gradient Boosting Machine), Sequential Backward Selection (SBS), and a metaheuristic technique termed Whale Optimization (WO) plays a decisive role in detecting Thyroid disease. The objective of this research is to provide an impeccably clean and rational way to detecting thyroid disorder in humans. Numerous processes have been applied to thyroid data sets with astounding results, yet the data used for thyroid disease detection is redundant, inconsistent and contains missing feature values, as per our literature survey. The proposed effort would develop an expert advisory system based on the Opti-LightGBM architecture for the purpose of detecting thyroid disorders and initiating necessary medication promptly. The proposed Opti-LightGBM model yields an accuracy of 99.75% on Thyroid dataset and outperforms several state-of-art comparative models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Classification Feature selection LightGBM Optimization Thyroid

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COVID-19 vaccine sentiment analysis using public opinions on Twitter

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Materials Today: Proceedings 2022年 64卷 448-451页

作者： Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Management Studies M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs. © 2022

关键词： Vaccines

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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A novel finite-time complex-valued zeoring neural network for solving time-varying complex-valued Sylvester equation

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Journal of the Franklin institute 2023年第2期360卷 1344-1377页

作者： G, Sowmya V, Shankar P, Thangavel Department of Computer Science Guindy Campus University of Madras Tamil Nadu Chennai600025 India Ramanujan Institute for Advanced Study in Mathematics University of Madras Tamil Nadu Chennai600005 India Department of Computer Science and Department of Network Systems and Information Technology Guindy Campus University of Madras Tamil Nadu Chennai600025 India

A novel finite-time complex-valued zeroing neural network (FTCVZNN) for solving time-varying Sylvester equation is proposed and investigated. Asymptotic stability analysis of this network is examined with any general activation function satisfying a condition or with an odd monotonically increasing activation function. So far, finite-time model studies have been investigated for the upper bound time of convergence using a linear activation function with design formula for the derivative of the error or with variations of sign-bi-power activation functions to zeroing neural networks. A function adaptive coefficient for sign-bi-power activation function (FA-CSBP) is introduced and examined for faster convergence. An upper bound on convergence time is derived with the two components in the function adaptive coefficients of sign-bi-power activation function. Numerical simulation results demonstrate that the FTCVZNN with function adaptive coefficient for sign-bi-power activation function is faster than applying a sign-bi-power activation function to the zeroing neural network (ZNN) and the other finite-time complex-valued models for the selected example problems. © 2022 The Franklin institute

关键词： Asymptotic stability

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An Automated Detection System of Autism Spectrum Disorder Using Optimal Feature Selection-Based Adaptive RNN with Attention Mechanism

An Automated Detection System of Autism Spectrum Disorder Us...

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International Conference on computer and Communications Technologies (ICCCT)

作者： J. Jegan Amarnath S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies Chennai Tamilnadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The research introduces an automated system for ASD detection through the implementation of an adaptive RNN combined with optimal features selection together with an attention mechanism. The system operates to detect ASD early through a data-driven methodology by analysing behavioural, visual and auditory indicators that mark symptoms of ASD. This solution provides accurate diagnosis and personalized care that doctors can use because it operates across many scales. The system accepts multiple forms of data from facial expressions together with speech patterns and motor behavioural data. A combination of feature selection methods identifies essential data attributes within raw information to decrease complex dimensions that improves system operational speed. The adaptive RNN model extracts temporal relationships through its learning process and the attention mechanism allows the model to select matching features to detect subtle changes in ASD behaviour properly. The framework adapts automatically because it uses available data to refine itself with a purpose of refining detection accuracy. Research outcome shows that adding the attention mechanism enables the model to select important attributes leading to better detection precision. The feature selection system adds to model performance optimization because it enables the model to concentrate on important signals while reducing potential overfitting problems. The system can apply in real time through its diagnostic excellence which minimizes both false positive and negative results. The proposed automated system represents a major step forward for early ASD detection since it delivers clinicians an effective instrument for early intervention. The precise results of this model stem from its adaptive learning system as well as its attention-based focus which leads to better ASD diagnosis and treatment planning outcomes.

关键词： Adaptation models Autism Visualization Attention mechanisms Recurrent neural networks Adaptive systems Accuracy Computational modeling Variable speed drives Feature extraction

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Solving Non-IID in Federated Learning for Image Classification using GANs 20

Solving Non-IID in Federated Learning for Image Classificati...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Chuenbubpha, Thiti Boonchoo, Thapana Haga, Jason Rattanatamrong, Prapaporn Thammasat University Department of Computer Science Pathum Thani Thailand National Institute of Advanced Industrial Science and Technology Tsukuba Japan

ISBN: (纸本)9798350300505

Federated Learning (FL) has emerged as a powerful methodology for training centralized models while preserving data privacy by using trained parameters from local models that are distributed among decentralized sites. Despite its growing popularity in the development of cloud-based Internet of Things (IoT) applications, FL performance is significantly impacted when data is non-independently and identically distributed (non-IID). This paper proposes a novel framework, called GANs Augmented IID - Federated Learning (GAIID-FL) to tackle the diversity of data distribution among clients in FL. The GAIID-FL framework collaboratively trains Generative Adversarial Networks (GANs) and then employs the trained GANs models to generate synthetic data and distribute proportionally to each device. Using GAIID-FL effectively restores the IID data distribution for the setting. The experimental results demonstrate that our framework can achieve up to 45% improvement in accuracy. In addition, the batch collaborative training approach for GANs models can reduce communication overhead by up to 90 times when compared to the unoptimized method. © 2023 IEEE.

关键词： Data privacy

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Potential Impacts of Online-Based Learning 2.0 and Certification on Employability 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Saini, Dilip Kumar Jang Bahadur Singh, Sumeshwar Negi, Neerja Bhandari, Sonam Kumar, Raj Joshi, Kapil Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Dehradun India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Rapid advancement in technology innovation has seen an expansion in online-based learning programs as a major aspect of numerous organizations’ course offerings around the globe. Online learning in colleges has gotten more interactive, adaptable, collaborative, and participative. It has gained a fundamental part to play later on as a classroom teaching tool and a self-study platform for skill improvement. The reason for this examination is to consider the likely effects of online-based learning and accreditation regarding employability. The term employability implies a set of abilities, understanding, and individual qualities that make a graduate bound to pick up work and prevail in their chosen occupation. This paper has especially investigated the issues of online certification real availability, their impacts on employability, and their overall points of interest and weaknesses. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented in the final stages of this research paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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