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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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Performance Analysis of Deep Learning Pretrained Image Classifiation Models 1

Performance Analysis of Deep Learning Pretrained Image Class...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Deepa Alli, A. Sheetal Prasad Km, Shiva Christ University Department of Computer Science Bengaluru India Presidency College Department of Computer Applications Bengaluru India Presidency College Department of Computer Science and Engineering Bengaluru India Rao Bahadur y Mahabaleswarappa Engineering College Department of Computer Applications Karnataka Ballari India

ISBN: (纸本)9798350316469

Convolutional Neural Networks (CNNs) is revolutionized in the field of computer vision, with the high accuracy and capability to learn features from raw data. In this research work focused on a comparative analysis of two popular CNN architectures, VGG16 and VGG19. The CIFAR dataset consists of 60,000 images, each with a resolution of 32x32 and it's belong to one of the 10 classes. Experimental results are compared with VGG16 and VGG19 in terms of their accuracy and training time, and to identify any differences in their ability to learn features from the CIFAR-10 dataset. The results of this research can aid in directing the choice of appropriate architectures for image classification tasks as well as the advantages of optimisation strategies for enhancing the efficiency of deep learning models. In order to enhance the performance of these structures, more optimisation methods and datasets may be investigated in subsequent research. © 2023 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning Image classification

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Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI 5

Insightful Clinical Assistance for Anemia Prediction with Da...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Anemia among young people has positive correlation with susceptibility and impede cognitive *** per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia. © 2024 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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Effective and Efficient Video Compression by the Deep Learning Techniques

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computer Systems science & engineering 2023年第5期45卷 1047-1061页

作者： Karthick Panneerselvam K.Mahesh V.L.Helen Josephine A.Ranjith Kumar Department of Computer Applications Alagappa UniversityKaraikudiIndia Department of Computer Science and Engineering lovely professional UniversityPhagwaraPunjabIndia School of Business and Management Christ UniversityBengaluruKarnatakaIndia

Deep learning has reached many successes in Video *** has become a growing important part of our daily digital *** advancement of better resolution content and the large volume offers serious challenges to the goal of receiving,distributing,compressing and revealing highquality video *** this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask,which creatively combines the Deep Learning Techniques on Convolutional Neural Networks(CNN)and Generative Adversarial Networks(GAN).The video compression method involves the layers are divided into different groups for data processing,using CNN to remove the duplicate frames,repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory(LSTM).Instead of the complete image,the small changes generated using GAN are substituted,which helps with frame-level *** wise comparison is performed using K-nearest Neighbours(KNN)over the frame,clustered with K-means and Singular Value Decomposition(SVD)is applied for every frame in the video for all three colour channels[Red,Green,Blue]to decrease the dimension of the utility matrix[R,G,B]by extracting its latent *** frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original *** experiments on several videos with different sizes,duration,Frames per second(FPS),and quality results demonstrated a significant resampling *** normal,the outcome delivered had around a 10%deviation in quality and over half in size when contrasted,and the original video.

关键词： Convolutional neural networks(CNN) generative adversarial network(GAN) singular value decomposition(SVD) K-nearest neighbours(KNN) stochastic gradient descent(SGD) long short-term memory(LSTM)

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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Dragonfly Algorithm and Variants for Feature Selection: A Review

Dragonfly Algorithm and Variants for Feature Selection: A Re...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Prinson, P.T. Geetha, Angelina Hindustan Institute of Technology and Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314946

Ultimately, the key to successfully analysing real-world problems is to be diligent and thorough. It is important to carefully consider all available data, remove any redundant or unnecessary information, and approach the analysis with an open mind. By doing so, it is possible to gain valuable insights and make informed decisions that can help solve complex real-world problems. By removing redundant, irrelevant, and noisy data, feature selection (FS), a difficult machine learning problem, attempts to lessen the number of features while maintaining a decent degree of classification accuracy. Feature selection is intended to reduce features. This immediately reduces the search space and aids machine learning algorithms in using only the most significant features. A review of Dragonfly algorithms and variants is presented in this paper, emphasizing their main characteristics. Eventually, the conclusion concentrates on the present research on Dragonfly Algorithm, emphasizing its limitations with suggested solutions that lead to future directions © 2023 IEEE.

关键词： Swarm intelligence

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Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

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computer Systems science & engineering 2023年第6期45卷 2351-2362页

作者： S.Aanjanadevi S.Aanjankumar K.R.Ramela V.Palanisamy Department of Computer Applications Alagappa UniversityKaraikudi630003TamilnaduIndia Department of Computer Science and Engineering Sri Raaja Raajan College of Engineering and TechnologyAmaravathipudur630301TamilnaduIndia Department of Electrical and Electronics Engineering Ultra College of Engineering and TechnologyMadurai625020TamilnaduIndia

Due to the enormous usage of the internet for transmission of data over a network,security and authenticity become major *** challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database *** describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical(Face,fingerprint,Ear etc.)and behavioural(Gesture,Voice,tying etc.)by means of matching and verification *** this work,biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential *** proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary *** Face Attribute Convolutional Neural Network(FACNN)is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further *** Artificial Intelligence(AI)into the proposed system,automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while *** codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and *** main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify *** system enhances the system performance by 8%matching and verification and minimizes the False Acceptance Rate(FAR),False Rejection Rate(FRR)and Equal Error Rate(EER)by 6 times and increases the data privacy through the biometric cryptosystem by 98.2%while compared to other work.

关键词： FACNN biometric cryptography AI FAR FRR EER

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Machine Learning for Recognition of Handwritten Devanagri Character 1

Machine Learning for Recognition of Handwritten Devanagri Ch...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tyagi, Shilpa Dutta, Chiranjit Singh, Manu SRM Institute of Science and Technology Department of Computer Applications Uttar Pradesh Ghaziabad201204 India SRM Institute of Science and Technology Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201204 India ABES Engineering College Department of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350353648

A framework known as optical character recognition is capable of converting handwritten or printed pictures into machine-editable structures. Several Indian dialects, including Hindi, Nepali, Marathi, Sindhi, and others, use the Devanagari script. In India, Hindi is the national language and the language that is spoken by most people. This script organises the development of the language. Nowadays, people are interested in gathering data in sophisticated configurations that are available in paper archives and then employing a search process to reuse this data later. In this study, we provide a novel approach to Devanagari script character recognition for printed Hindi characters. The principal objective of this research is to identify the consonants and the vowels, which can subsequently be used for recognizing the complicated inferred words. The primary focus of this project is on the identification of each individual consonant and vowel. Several pre-processing techniques, including feature extraction, segmentation, and classification, have been researched and used in this project to create an advanced Hindi OCR system. The K-NN classification technique was used in earlier research;however, in the work that is being suggested, we have employed a hybrid technique that combines neural networks with K-NN. The recognition rate of the suggested approach is 97.4%, whereas that of the existing techniques is 94.5%. This suggests that the approach which proposed here is superior to techniques employed in the method which already exist. © 2024 IEEE.

关键词： Support vector machines

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Improved AO based Classification Framework for Eye Diseases 2

Improved AO based Classification Framework for Eye Diseases

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Saju, Binju Asha, V. Mathew, Jincy C Sangma, Sengcheng Ghosh, Shouvik Sivaguru, K. Adi Shankara Institute of Engineering & Technology Department of Computer Science - AI Engineering Kerala Kalady India New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350327533

A sensory organ that reacts to visible light, the human eye is a component of the sensory nervous system. A new approach is needed since, although physical detection of the eye condition is still a possibility, it is challenging and time-consuming. The study recommends a hybrid paradigm to classify various eye illnesses. Prior to further processing, pictures are first reduced in size, made grayscale, and noise-free using a combination of the Weiner and Median filters. Histogram equalization based on a Gaussian Mixture is also used to enhance pictures. Picture features are recovered with the aid of a thick convolutional neural network. An improved Aquila optimized ResNet101 model is used for classification. The effectiveness of the models is contrasted with that of related studies in the literature. A F1 score of 98.18%, accuracy of 99.2%, sensitivity of 98.9%, specificity of 98.91%, and precision of 98% were attained by the suggested model. © 2024 IEEE.

关键词： DCNN Diabetic retinopathy Gaussian Mixture IAO ResNet

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Optimizing Quality of Service in IoMT Through Fog Computing-Enabled Service Composition 13

Optimizing Quality of Service in IoMT Through Fog Computing-...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Anand, Dinesh Kaur, Avinash Singh, Parminder School of Computer Applications Lovely Professional University Punjab Phagwara India Guru Nanak Dev Engineering College Department of Computer Applications Punjab Ludhiana India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350380583

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, allowing for remote patient monitoring and personalized treatments. However, due to the resource limitations of medical devices, the vital nature of health- care data, and the necessity for real-time response, guaranteeing Quality of Service (QoS) in IoMT systems offers considerable issues. This research presents a technique for addressing these difficulties in IoMT by exploiting Fog Computing-Enabled Service Composition. The issue statement centers on the need to improve QoS in IoMT systems, which includes response time, reliability, and energy consumption. Existing solutions struggle to match the severe criteria of IoMT, where service deployment, orchestration, and data processing must be performed in a fast, energy-efficient, and reliable way. To address these concerns, we propose a set of techniques that combine Fog Computing with service composition to optimize QoS in IoMT. The framework is proposed to incorporate dynamics service orchestration, context ware decision-making and balance the load into the IoMT systems to allow service composition at network edge. The implementation of proposed technique is done and demonstrates how computing nodes are worked inside the IoMT system to enable dynamic service composition. Our results show improvements in the QoS parameters like response times, optimized energy consumption and enhanced reliability. © 2024 IEEE.

关键词： Fog Computing Internet of Medical Things Quality of Service Service Composition

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