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A holistic securing approach to speech steganography system with message embedding and pause optimization

Multimedia Tools and applications 2025年 1-27页

作者： Kumar, Surendra Yadav, Arun Singh Dwivedi, Mridula Kumar, Mohit Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281401 India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow226007 India Lucknow India Department of IT Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

Nowadays, prevention control of message sharing as well as security enhancement in speech transmission which occurs due to users having a passion for novel inventions, communication system benefits. In this paper, the main objective is to design a speech steganography framework with covert message data transmission including speech. The secret message data is encrypted with the help of the Hyperelliptic Curve Cryptography (HECC) algorithm to provide a maximum security environment. The designed speech steganography framework is integrated with the pause removal mechanism respectively. Since, the Stegospeech is sent into the channel, as the Stegospeech as of now obtained the secret Message data its transfer speed will increment. Then at that point, it needs high bandwidth for transmission as well as needs higher energy utilization. In this manner, to decrease the data transfer speed, the best procedure is to eliminate the undesirable pause in the speech, the pause removal strategy carried out utilizing the combinations of Ensemble Empirical Mode Decomposition (EEMD) with Hilbert-Huang Transform (HHT). Then, some techniques are used to embed the message into the speech like 4-level Discrete Wavelet Transformation (DWT), Matrix definition, and Single Value Decomposition (SVD). They are likewise utilized for the secret message extraction from speech signal as well as message information from the Stegospeech signal. The presented strategy is carried out and analyzed towards the different invasions in the output. The Correlation Coefficient (CC), the Peak Signal-To-Noise Ratio (PSNR), the Signal-To-Noise Ratio (SNR), the Number of Pixels Changing Rate (NPCR), and the Unified Average Changing Intensity (UACI) are some of the popular standards that are utilised when comparing two encrypted voice signals. The numbers 33.3% and 100%, respectively, for UACI and NPCR are considered to be optimum states. It is noticed that the presented technique provides better Statistical resul

关键词： Steganography

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A Study on Swarm-Based Approaches for Intrusion Detection System in Cloud Environment

A Study on Swarm-Based Approaches for Intrusion Detection Sy...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Nishika Solanki, Kamna Dalal, Sandeep University Institute of Engineering and Technology MDU Rohtak India Department of Computer Science and Applications MDU Rohtak India

ISBN: (纸本)9789819909803

Cloud is an advanced Internet of Things enabled network. The layered and distributed nature of this network opens this network to the public environment and varied kind of users. Bulk of data is stored on centralized system, and large amount of information is communicated in the real environment. This public and large amount of shared data and communication opens the network for intruders. Various intrusion detection systems were designed and implemented on different layers of the cloud network to detect and prevent different kinds of internal and external attacks. In the recent years, various swarm-based methods were integrated within intrusion detection system to optimize the performance. In this paper, a detailed study and review of cloud- and Internet of Things-based intrusion detection systems is presented. The analytical and descriptive study of swarm-based intrusion detection system is provided in this paper. This study identifies that the swarm integration within intrusion detection and classification methods improved the performance and reliability of conventional systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD and BRP 3rd

AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD ...

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3rd International Conference on Advanced Computing and applications, ICACA 2024

作者： Jana, Manasi Jana, Biswapati Joardar, Subhankar Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India

ISBN: (纸本)9789819747986

Secret data hiding in compression domain reduces the transmission cost and also ensures more security than spatial and transform domain. In this paper, we present a data concealing strategy on the compression domain that delivers high embedding capacity (EC) while also improving the quality of transformed images. To compress the original image, a modified AMBTC method is used for its low time complexity. Modified AMBTC is used to transform one bit plane into two bit plane, which enhances the quality of the stego image. Three levels of security are applied on secret data to make the scheme more secure. Data are embedded into two adjacent pixels of each 1×2 block using PVD method. The robustness of the scheme is analysed by applying unknown permutation, unknown shared key and unknown iteration steps. The experimental results reveal that the suggested method outperforms the previous schemes in terms of embedding capacity and imperceptibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ambtc BRP Data hiding PSNR PVD

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An Imperial Analysis of Large Language Models for Automated Tweet Sentiment Prediction

An Imperial Analysis of Large Language Models for Automated ...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Akhouri, Shivam Ajbani, Vidhi Lakshminarayanan, Ritika Pandey, Trilok Nath Nigam, Meher Shrishti Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350313987

Company' s brand perception majorly depends on customer experience and the reviews which follow that. A customer is capable of influencing many more people just on the basis of reviews given by him/her. Google released Pathways Language Model (PaLM) which is a major advancement in Artificial Intelligence (AI). It has been trained with the Pathways System, which allows it to generalize tasks in various domains. In this work, a trustworthy platform is provided for the examination of millions of people's continually moving and changing perspectives. Twitter data is captured, and effective sentiment and data analysis is used to generate trustworthy and helpful info graphics reflecting public opinion. Product sales, stock returns, election outcomes, and other commercial and social events may all be predicted and explained using the information found in tweets. Brands, product manufacturers, and other companies may utilise the information derived from data analysis to better understand their brand image, expand their market share by targeting the relevant demographics at the right moments, and enhance their offerings in terms of both quality and customer service. © 2023 IEEE.

关键词： Attention Units Data visualization Masks Sentiment Analysis Tokenization

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Predicting Future Automobile Ownership Trends in Uzbekistan Using Linear Regression 2nd

Predicting Future Automobile Ownership Trends in Uzbekistan ...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Ather, Danish Singh, Malikhan Arora, Kavita Pathak, Sonal Qurashi, Suhail Javed Singh, Anupam Amity University Tashkent Uzbekistan Computer Science and Engineering College of Engineering & Technology Mathura India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819735938

The rapid urbanization and economic growth of Uzbekistan over the past decades have engendered significant changes in automobile ownership trends. Understanding these trends is crucial for infrastructure planning, sustainable transportation strategies, and environmental considerations. This Paper seeks to employ Machine Learning (ML) techniques to forecast the number of cars per 100 households in Uzbekistan by 2025, based on historical data from 2010, 2015, and 2020. The dataset, derived from official statistics provided by the State Committee of the Republic of Uzbekistan on Statistics, shows a remarkable increase from 21 cars per 100 households in 2010 to 48 by 2020. Leveraging regression models, we aim to predict the expected growth for 2025. Preliminary analyses hint at a continuing upward trend, impacted potentially by factors such as Population growth, GDP growth, urban migration, and increasing global connectivity. The findings from this research will not only serve as a prediction tool but will also shed light on the socio-economic factors influencing car ownership, providing valuable insights to policymakers and urban planners. While the limited data points present a challenge for complex model utilization, the study underscores the potential of ML in forecasting and the need for more comprehensive data collection in future studies. The predictive insights drawn from this research can significantly aid in shaping transportation policies, mitigating potential environmental impacts, and planning urban infrastructures in Uzbekistan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain Technology in Smart Healthcare 2

Preserve the Medical Data using Secure Partially Homomorphic...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Kavitha, S. Pavithra, B.S. Shashikala, A.B. Jagadeesh, B.N. Mageswari, P. Uma Marichi, Dileep Nitte Meenakshi Institute Of Technology Department Of Mca Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Rns Institute Of Technology Department Of Information Science And Engineering Bengaluru India Presidency College Department Of Computer Applications Bangalore India

ISBN: (纸本)9798350381665

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data. © 2024 IEEE.

关键词： Blockchain

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An Emperical Analysis of Machine Learning Algorithms for Used Car Price Prediction System

An Emperical Analysis of Machine Learning Algorithms for Use...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Bhatt, Nirav Sanjay Nath Pandey, Trilok Reddy, Suravaram Rishabh Jayasurya, B. Dash, Bibhuti Bhusan Shekhar Patra, Sudhansu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350308167

The market for used cars is a dynamic and ever-expanding industry, with various factors influencing the pricing of pre-owned vehicles. Accurate pricing is crucial for both buyers and sellers, as it ensures fair transactions and minimizes the risk of financial loss. This paper introduces a machine learning algorithms developed for the purpose of predicting used car prices based on a comprehensive set of features. Various machine learning (ML) techniques such as multiple linear regression, ridge regression, lasso regression, Bayesian regression, Decision tree regression, random forest regression, eXtreme Gradient Boost (XGB) regression, Light Gradient Boosting Machine (LGBM) regression, Gradient boosting regression and Multilayer Perceptron (MLP) regression. To assess the algorithm's performance, rigorous evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) score has been employed. © 2023 IEEE.

关键词： ANN Bayesian Regression Decision Tree Regression Lasso Regression LGB Linear Regression MLP Random Forest Regression Ridge Regression XGB

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Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

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Recent Advances in computer science and Communications 2024年第4期17卷 40-53页

作者： Murugan, Ramalingam Paliwal, Manish Patibandla, Rama Seetha Maha Lakshmi Shah, Pooja Balaga, Tarakeswara Rao Gurrammagari, Deepti Raj Singaravelu, Parvathavarthini Yenduri, Gokul Jhaveri, Rutvij School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India AP India Kallam Haranadhareddy Institute of Technology Andra Pradesh Guntur India Department of Computer Applications Kongu Engineering College Erode Perundurai India

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem. © 2024 Bentham science Publishers.

关键词： Knowledge management

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Comprehensive Survey on Internet of Medical Things (IoMT) - applications and Challenges 15

Comprehensive Survey on Internet of Medical Things (IoMT) - ...

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

作者： Anusha, V. Sai Kumar, R. Kiran Kumar, G. Charan Mabjan, Palagiri Madanapalle Institute of Technology & Science Department of Electronics and Communication Engineering Madanapalle517325 India Annamacharya University Department of Computer Applications Rajampet India

ISBN: (纸本)9798350370249

The Internet of Medical Things (IoMT) is changing healthcare through the use of the Internet to connect medical devices, apps, and systems. This helps improve patient care, makes healthcare more efficient, and improves health outcomes. This paper deals with the edge-fog-cloud computing architectures for Internet of Health applications. Remote patient monitoring is done by collecting the data from wearable health devices, processing at different communication levels, applying intelligent algorithms and providing insights to the patient and healthcare professionals. This paper reviews the IoMT architecture and how the entire workflow is managed at device, edge, fog, and cloud levels. The paper also discusses IoMT challenges, such as data security and privacy concerns due to the interconnectedness and interoperability of devices at each layer and hurdles for seamless data integration within healthcare networks. © 2024 IEEE.

关键词： Electronic health record

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Predicting Customer Satisfaction in Brazil E-commerce: A Comparative Study of Machine Learning Techniques 4

Predicting Customer Satisfaction in Brazil E-commerce: A Com...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pandey, Trilok Nath Vasudev, Anirudh Sagayanathan, Dennis Anjan, Govind Arshad, Danish Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350306118

Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online industries in the world which till date is not completely understood from a customer's standpoint. Brazil being a developing country is the 12th largest market for e-commerce in the world and accounts for 30 % of e-commerce in Latin America and remains largely unexplored. In this study, we used machine learning (ML) techniques such as LSTM, XGBClassifier, Logistic Regression, Neural Network, and Random Forest to predict customer satisfaction using a retail dataset from Brazil. We contrasted the efficacy of these models using a variety of evaluation criteria, such as precision, recall, and Fl-score. The LSTM model had the highest accuracy with a score of 0.89, followed by the XGBClassifier with 0.83, Naive Bayes with 0.73, Logistic Regression with 0.74, Support Vector Classifier with 0.74, and Random Forest with 0.78. The superior performance of the LSTM model over other models suggests that it has superior predictive abilities. © 2023 IEEE.

关键词： Customer satisfaction

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