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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sudhan, Surabhi Nair, Parvathy P. Thushara, M.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Applications Amritapuri India

ISBN: (纸本)9798350394474

The convergence of Text-to-Speech (TTS) and Speech-to-Text (STT) models has spearheaded an era of transformative advancements in natural language processing (NLP). This article discusses transformative advances in natural language processing (NLP). It presents a comprehensive survey that systematically examines specific STT and TTS models, such as rule-based systems and deep learning architectures, and explores their structures, methodologies, and applications. The paper classifies these models based on their underlying technologies, architectural approaches, training methods, and application domains, and provides a detailed overview of their development. It highlights industry successes and challenges, covering a range of application areas, from assistive technologies to virtual assistants and language translation. This research provides new perspectives to understand the current world of STT and TTS technologies, which will especially benefit researchers, developers, and industry players. By highlighting the empowering bridge between written and spoken language, the paper uniquely contributes to the ongoing dialogue in the field. It not only sheds light on the current state of STT and TTS technologies but also lays the groundwork for future innovations and advances that could change the way we interact with language. © 2024 IEEE.

关键词： Deep learning

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EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle via Edge Devices 15

EdgeShield: Hybrid Real-Time Attack Detection in IOTA Tangle...

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

作者： Arunodhai, V. Susan, Leena P. Kannimoola, Jinesh M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350370249

The Internet of Things (IoT) revolutionizes technology interaction by enabling seamless connectivity and automation, but it introduces significant security challenges. A distributed ledger technology (DLT) called IOTA Tangle, a utilizing a Directed Acyclic Graph (DAG), enables secure and feeless transactions among IoT devices. Despite its advantages, the IOTA Tangle faces threats such as replay attacks and double spending, which highlight vulnerabilities in distributed ledger technologies. To address these security concerns, we initially developed EdgeGuard, a rule-based system for spam detection within the IOTA ecosystem. However, its reliance on predetermined rulesets limited its adaptability. We have now enhanced our approach with EdgeShield, incorporating a neural network for superior threat *** enhances adaptability over EdgeGuard in threat detection for the IOTA Tangle by integrating machine learning. EdgeShield analyzes network packets to identify threats, leveraging machine learning to adapt to evolving risks. Trained using the MQTTSet dataset, it detects network-based threats such as denial-of-service (DoS) attacks and packet flooding. Tested with simulated IoT data, EdgeShield demonstrates effectiveness across various domains, particularly in healthcare IoT. Its decentralized processing on edge devices like the Raspberry Pi 4 enables immediate network traffic analysis, reducing reliance on centralized servers and improving system resilience. Additionally, real-time alerts empower administrators to proactively secure IoT deployments and maintain transaction integrity within the IOTA Tangle. This paper traces our research evolution from EdgeGuard to EdgeShield, highlighting advancements and demonstrating the enhanced security of IoT transactions on the IOTA Tangle. © 2024 IEEE.

关键词： and Decentralised Security Measures Distributed Ledger Edge Computing Edge Devices Internet of Things (IoT) IOTA Tangle Machine Learning Network Packet Analysis Real-Time Security

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A Comprehensive Survey on Sorting Permutations using Transposition Tree 5

A Comprehensive Survey on Sorting Permutations using Transpo...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Ashtapadhi, S.V. Pillai, Devi Indulekha, T.S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350376685

This paper provides a survey on the use of transposition trees for sorting permutations. A permutation can be represented as, π = {n,n - 1,...,1} and its identity as, I = {1,2,...,n - 1,n}. Transforming π to I is termed permutation sorting and it can be done using various operations. A transposition tree is one such operation that offers a structured approach to permutation sorting, facilitating in-depth analysis of permutation structures. A transposition tree T=(VT, ET) is a spanning tree that gives a higher level of abstraction compared to Cayley graphs. We can place Tokens/Markers on vertices and each edge is a transposition. Research in sorting permutations plays a crucial role in simplifying complexities within real-world domains, particularly in areas such as the design of computer interconnection networks and genomic studies. Effective permutation sorting techniques are important for identifying genetic mutations and optimizing network performance. This leads to the emergence of the problems, of finding the distance between the source permutation π and its identity I and finding the diameter of the underlying Cayley graph. Due to the complexities of the underlying Cayley graph, sorting using a transposition tree is an NP-Hard problem. However, optimal solutions are known for certain classes of transposition trees. This paper aims to provide insights into the known classes of transposition trees such as path, star, brooms and their corresponding results for the distance and diameter problems, where available. Additionally, relevant works related to this domain are mentioned to provide a better understanding of advancements in this area. © 2024 IEEE.

关键词： Interconnection networks (circuit switching)

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Explainable AI for Health Care based Retrieval System 15

Explainable AI for Health Care based Retrieval System

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Reghu, Lakshmi Ashok, Gayathri Menon, Remya R.K. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798331300579

Explainable Artificial Intelligence or (XAI) be-coming more popular in machine learning field because it allows the users to interpret most clear result and also provide public trust and facilitate their development and deployment of AI *** AI models to be more transparent and understandable is the aim of the burgeoning field of explainable ***, we cover a varied scope of XAI for a healthcare-based retrieval system. When a user queries a healthcare-related document, XAI may explain why it was retrieved. Patients who make use of the XAI tools will be better able to comprehend the advice and be more inclined to believe it. The article also covers several additional uses for XAI models, including manufacturing and banking. Within the healthcare industry, medical diagnostic systems' predictions may also be defined by using explainable AI models. This is crucial for applications in the healthcare industry since it's required to comprehend the reasons behind predictions made by AI. © Grenze Scientific Society, 2024.

关键词： Diagnosis

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Knowledge Extraction from Distributed Heterogeneous Data Sources 5

Knowledge Extraction from Distributed Heterogeneous Data Sou...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Ramachandran, Raji Abhijith, K. Karthik, J.J. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361155

The growing volume and complexity of unstructured and semi-structured data pose a significant challenge in extracting meaningful and relevant information. Information Extraction (IE) emerges as a powerful technique to transform unstructured data into structured format, facilitating further analysis and knowledge discovery. IE techniques can effectively extract information from unstructured data in the form of entities, relationships, objects, and events. Our objective is to develop a method for extracting or filtering out the most relevant information from data sources which are heterogeneous in nature as well as they are distributed over many systems and hence build a unified schema based on the collective knowledge extracted from the data sources. Several existing methods have been developed to extract knowledge from heterogeneous datasets. We focuses on datasets in the medical field that are distributed over multiple systems. Specifically, we aim to extract valuable information from patient records, including patient details, medical conditions and medications consumed. The unified schema can be utilized to study a new patient's condition and assist doctors in making a pre-examination of a patient by querying it. © 2024 IEEE.

关键词： Data mining

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Data Mining Approach for Early Detection of Anemia Using Predictive Apriori Algorithm and Boolean Matrix Analysis 3rd

Data Mining Approach for Early Detection of Anemia Using Pre...

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

作者： Bhargava, Ritu Dixit, Pooja Rathore, Pramod Singh Goswami, Ankur Ajmer India Department of Computer Science Dezyne College Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Governmentt Engineering College Bikaner India

ISBN: (纸本)9789819751457

Paper deals with the predictive apriori algorithm for a disease related to hemoglobin blood test data. The dataset has been collected from public dataset. The dataset was pre-processed to remove missing and unwanted data. In this paper, Boolean matrix is defined for eliminating the candidate from the subset, which is not frequent. In the Boolean matrix, frequent patterns are generated using "AND" operation. Since the anemia is a common health problem which affects mostly women worldwide. The approach is used in this paper for detecting the anemia disease from the result investigation. Predictive apriori algorithm and Boolean matrix give the better results. This paper is divided into four sections. Section one describes introduction of anemia and apriori algorithm. Section two deals with literature review. Section three describes the prediction algorithm and results, and lastly, section four discusses the conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Detection of Strabismus Using Convolutional Neural Network-Based Classification Models 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Subbulakshmi, S. Mani, Aditya Gupta, Divyam Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

Strabismus, a prevalent eye misalignment disorder, can lead to vision impairment if not promptly identified and treated. Existing automatic detection approaches often rely on single-feature methodologies, such as deep features or ratio features, which may suffer from reliability issues. This paper introduces a pioneering multi-feature fusion model (MFFM) aimed at augmenting the accuracy of strabismus detection that integrates deep features and ratio features extracted from corneal light reflection photographs using convolutional neural network (CNN) based on classification models. Initially, deep features are extracted employing pre-trained CNN models renowned for its efficacy in diverse image recognition and classification of strabismus images. Through the fusion of these features, MFFM offers a robust framework to bolster the accuracy of strabismus detection. This research presents a promising avenue for advancing early diagnosis and treatment of strabismus, thereby potentially mitigating the risks of vision impairment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Hybrid Technique for Approximating the Solution of Fractional Order Partial Integro-Differential Equations

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 139-149页

作者： Mohsin, Ahmed K. Abdulkhaleq, Fajir A. Mohammed, Osama H. Department of Mathematics College of Education Mustansiriyah University Baghdad Iraq Ministry of Education Baghdad Iraq Department of Mathematics and Computer Applications College of Science Al-Nahrain University Jadriya Baghdad Iraq

In this paper, we discuss the numerical solution of fractional order partial integro-differential equations. The type of fractional derivative used is a Caputo derivative. The method proposed in this paper known as transform optimal perturbation iteration method. This method combines the optimal perturbation iteration method and the Laplace transform in order to converge to the exact solution. The proposed method is highly efficient and provides the means of controlling the approximate solutions convergence. Illustrative examples prove that the suggested approach is very accurate when compared with the exact solution and the results of the existing methods. 2025 The Author(s).

关键词： Laplace equation

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Predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques 1

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International Conference on Internet of Everything and Quantum Information Processing, IEQIP 2023

作者： Baby Lavanya, K. Ismail Ansari, M.D. Gundu, Venkateswarlu Krishna Mohan, G. Mouleswararao, B. Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation AP Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (数字)9783031619298

ISBN: (纸本)9783031619281

Customer churn poses a significant challenge to the telecom industry as it directly impacts revenue and customer retention efforts. To tackle this concern, the present research suggests an all-encompassing machine learning oriented method for forecasting customer attrition within the telecommunications industry. The dataset is employed for training and testing the Machine Learning models, and their effectiveness is assessed through conventional metrics like accuracy, precision, recall, and the area under the curve of the receiver operating characteristic (AUC-ROC). Conducting an analysis of feature importance enables us to pinpoint the foremost factors that impact customer churn. The findings illustrate that the logistic regression model attains accuracy in its predictive capabilities for customer attrition. The approach offers a practical and accurate solution, aiding telecom companies in reducing churn, increasing customer loyalty, and ultimately improving business performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Telecommunication industry

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An Elegant Evaluation: Triangulating Clustering Methods for Customer Segmentation 2

An Elegant Evaluation: Triangulating Clustering Methods for ...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Manimozhi, S. Ruby, D. Biruntha, K. Periyar Maniammai Institute of Science & Technology Department of Computer Science and Applications Thanjavur Tamilnadu Vallam613403 India

ISBN: (纸本)9798350328202

In the Business world, the customer satisfaction is vital. In order to improve the sales and patterned the satisfaction of customer, the preferences of the customer, quality and quantity of the product is analyzed. Various parameters are used in business aiming to improve the sales and customer satisfaction. The Agglomerative clustering, K-Means Clustering and Density Based Spatial Clustering (DBSCAN) techniques are used in this study. In the Density based clustering techniques used to fragment the customers based on the customer preference and behavior. The Intra-cluster variance is minimized with K-Means clustering. By employing the three algorithms, the customers are categorized based on who share similar interests by extracting and analyzing patterns from the customer data at hand. The evaluation reveals that K-means Clustering and Agglomerative Clustering achieves the highest silhouette score when conducting customer segmentation, surpassing other methods. The comparative analysis conducted in this study on feature-based customer segmentation provides organizations with valuable insights for implementing effective strategies in customer segmentation. © 2024 IEEE.

关键词： K-means clustering

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