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Comparative Analysis of Execution of CNN-Based Sanguine Data Transmission with LSB-SS and PVD-SS

computer Systems science & engineering 2023年第8期46卷 1707-1721页

作者： Alaknanda S.Patil G.Sundari Arun Kumar Sivaraman Department of Electronics and Communication Sathyabama Institute of Science and TechnologyChennaiTamil Nadu600119India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

The intact data transmission to the authentic user is becoming crucial at every moment in the current ***;is a technique for concealing the hidden message in any cover media such as image,video;and audio to increase the protection of *** resilience and imperceptibility are improved by choosing an appropriate embedding *** paper gives a novel system to immerse the secret information in different videos with different *** audio and video steganography with novel amalgamations are implemented to immerse the confidential auditory information and the authentic user’s face image.A hidden message is first included in the audio from the multimedia file;using LSB *** Stego-video is created in the second stage by merging the authorized user’s face into the frame of the video;by using PVD ***-audio is linked again with the stego-video in the third *** incorporated perspective techniques(LSB-SS and PVD-SS algorithms)with more significant data immersing capacity,good robustness and imperceptibility are proposed in this research *** spread spectrum approach is used to increase the complexity of secret data *** different video files are tested with different voice files with the results such as PSNR,SSIM,RMSE and MSE as 52.3,0.9963,0.0024 and 0.0000059,respectively.

关键词： Audio steganography data hiding information security pixel value differencing

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Capsule Network Approach for Image Classification 4th

Capsule Network Approach for Image Classification

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Marjit Singh, Moirangthem Kumar Sarkar, Nayan Department of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Arunachal Pradesh Itanagar India

ISBN: (纸本)9789819994410

Numerous deep learning (DL) models based on convolutional neural network (CNN) have been developed for image classification. Though the CNN models attained outstanding performance, its architecture contains certain limitations such as loss of information in the pooling layer and no spatial relationship among the features of an object. The development of capsule network (CapsNet) has overcome the drawbacks of CNN. The CapsNet works on a set of capsules, and each capsule comprises a set of features. The CapsNet consists of a convolution layer, a primary capsule layer, image caps and a fully connected (FC) layer. In this paper, a variant of CapsNet approach is proposed by tuning the parameters of the original CapsNet and replacing ReLU with AReLU activation function in each FC layer. The classification performance of the proposed approach is evaluated on the Plant Village (PV) and Tomato datasets. 80% of the datasets were trained and 20% were tested. The proposed approach considering filter size of 5 × 5 in the feature extraction layer with AReLU activation function in each FC layer delivers 94.02% and 94.06% classification accuracies on PV and Tomato datasets respectively. Compared to other modern approaches, the proposed approach delivered better results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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Brain Tumor Detection Using Xception Model

Brain Tumor Detection Using Xception Model

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Philip, Joshwa Joy Rani, Ruchi Kumar, Sumit School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Science and Engineering Pune India School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350388800

Numerous morphological and histological characteristics of brain tumors make diagnosis and treatment very challenging. Traditional diagnosis methods often depend on the analysis of medical images, which can lead to unclear conclusions and errors. In recent years, machine learning (ML) and deep learning techniques (DL) have shown great scope in improving the detection and classification of brain tumors. These methods extract the features and classify medical imaging data, such as MRI, CT, and PET scans. Convolutional and recurrent neural networks give better performance even though there are challenges in the dataset. In the proposed work, Xception architecture is used which is a type of convolution neural network. This model is pretrained on the ImageNet dataset. The proposed model achieves 99.90% accuracy in brain tumor detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Delay-Constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways 19th

Delay-Constrained Multicast Throughput Maximization in MEC ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Junyi Wei, Zhenchun Yuan, Xiaohui Lyu, Zengwei Feng, Lin Han, Jianghong School of Computer Science and Information Technology Hefei University of Technology Hefei China Department of Computer Science and Engineering University of North Texus DentonTX United States

ISBN: (纸本)9783031545306

Multi-access Edge Computing presents a compelling solution for delivering seamless connectivity to computing services. In this study, we aim to optimize multicast throughput to ensure high-quality experiences for passengers engaged in inter-train interactions within dedicated MEC networks designed for high-speed railways. Considering the unique challenges associated with high-speed railways, we model multicast routing paths as group Steiner trees. Subsequently, we devise a rapid tree construction method by converting the root search into the Generalized Assignment Problem (GAP). This innovative approach skillfully balances accuracy and computational efficiency. We demonstrate that this problem can be effectively reduced to the bounded knapsack problem with setups. In addition, we recognize the presence of precedence constraints between tasks and their respective outcomes. Consequently, we introduce a new variant of the knapsack problem, which we refer to as the Precedence-constrained Bounded Knapsack Problem with Setups. Our approach, termed the GAP- and knapsack-based Group Steiner Tree (GKGST), offers a relative performance guarantee of 1/2. We evaluate the GKGST algorithm against three baseline algorithms, which are adapted and extended from existing literature. Simulation results indicate that our proposed algorithm exhibits considerable potential for enhanced performance. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Computational efficiency

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Optimistic Multi-Agent Policy Gradient 41

Optimistic Multi-Agent Policy Gradient

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41st International Conference on Machine Learning, ICML 2024

作者： Zhao, Wenshuai Zhao, Yi Li, Zhiyuan Kannala, Juho Pajarinen, Joni Department of Electrical Engineering and Automation Aalto University Finland School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Aalto University Finland University of Oulu Finland

Relative overgeneralization (RO) occurs in cooperative multi-agent learning tasks when agents converge towards a suboptimal joint policy due to overfitting to suboptimal behaviors of other *** methods have been proposed for addressing RO in multi-agent policy gradient (MAPG) methods although these methods produce state-of-the-art *** address this gap, we propose a general, yet simple, framework to enable optimistic updates in MAPG methods that alleviate the RO *** approach involves clipping the advantage to eliminate negative values, thereby facilitating optimistic updates in *** optimism prevents individual agents from quickly converging to a local ***, we provide a formal analysis to show that the proposed method retains optimality at a fixed *** extensive evaluations on a diverse set of tasks including the Multi-agent MuJoCo and Overcooked benchmarks, our method outperforms strong baselines on 13 out of 19 tested tasks and matches the performance on the rest. Copyright 2024 by the author(s)

关键词： Multi agent systems

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AntiMicrobial Resistance Prediction Using Advanced Image Encoding Techniques 1

AntiMicrobial Resistance Prediction Using Advanced Image Enc...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Ghadekar, Premanand Jaggi, Arjun Swain, Debabrata Ghadekar, Aryan Vishwakarma Institute of Technology Department of Information Technology Pune India HCLTech CA United States Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331520540

Antimicrobial resistance (AMR) is one of the significant problems in the health sector and requires resources to create and develop accurate forecasting systems capable of recognizing resistant microbes and their treatment approaches. The traditional machine learning models used in AMR prediction usually suffer from poor genomic sequences feature extraction and finding it hard to solve big biological problems with big data. K-mer based methods or other simpler classifiers have been able to provide certain accuracy, but these have strived when dealing with many genomic strains and variants. To address these shortcomings, deep learning models are able to learn data representation at various abstraction levels which understanding complex data of fundamental and high dimensional patterns. In this work we provide a new deep learning approach exploiting CNNs on FCGR images of microbial genomes. The sequence is represented as frequency chaos game representation making it better for the enn structural application as resnet and densenet where features are deeply embedded in classifiers. When it comes to string and image based methods it is clear that they both have their own benefits and shortcomings. String based analysis is a faster and more accurate way to analyze naked sequence data conversion, but it may not be as informative as visual presentation. Our study verified the hypothesis that it is feasible to encode genomic data for further FCGR, so that the data might be utilized as an input for machine learning and deep learning methods. The model is effective with substantial improvements with the accuracy of over 90% in test dataset. This represents a significant improvement in accuracy score with better systems for predicting antibiotic resistance among many types of microbes. © 2025 IEEE.

关键词： AntiMicrobial Resistance Prediction CARD Frequency Chaos Game Representation microbes NCBI

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EmoCNN: Unleashing Human Emotions with Customized CNN Using Different Optimizers 2

EmoCNN: Unleashing Human Emotions with Customized CNN Using ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sahana, M. Umesh, Praneetha Kodipalli, Ashwini Rao, Trupthi Department of Computer Science & Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore India

A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better und

关键词： Emotion Recognition

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Enhanced ML Based Content-Based Image Retrieval for Mobile Devices

Enhanced ML Based Content-Based Image Retrieval for Mobile D...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Meenakshi, K.M. Singh, Kirti Dilip Batta, Priya Chauhan, Rakhi Bhargava, Deepshikha Lohani, Bhanu Prakash Amity School of Engineering & Technology Amity University Greater Noida India Chandigarh University Department of Computer Science & Engineering Mohali India Geeta University Department of Computer Science & Engineering Haryana Panipat India

ISBN: (纸本)9798350379716

Image retrieval systems based on content can be developed for mobile applications that provide users with a seamless and efficient way to search for images based on their content. The development of CBIR systems for mobile applications presents unique challenges and opportunities. Mobile devices typically have limited computational resources, storage capacity, and network bandwidth compared to traditional computing platforms. Therefore, CBIR systems designed for mobile applications must be lightweight, efficient, and optimized to run smoothly on these devices. The Adaboost algorithm progressively learns weak classifiers and aggregates them to construct a robust final classifier. Subsequently, a weak learner is introduced, and the data undergoes reweighting. Misclassified test images are assigned greater importance while correctly classified ones are downweighted. Consequently, subsequent weak learners prioritize the query images previously misclassified by their predecessors. The average precision is increased in further iterations to 95.6% from the initial iteration of 94.9%. Average recall shows a little more improvement from the previous set by increasing from round 1 with 94.1%, and increasing to 95.9% towards the last round of iteration. The F-Measure keeps fairly stabilized at 95.1478 after the first round. The time taken by every iteration reduces slightly from 3. © 2024 IEEE.

关键词： Content based retrieval

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Ransomware Detection Utilizing Ensemble Based Interpretable Deep Learning Model 2

Ransomware Detection Utilizing Ensemble Based Interpretable ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Chisty, Tanjir Alam Rahman Rahman, Md. Mahbubur University of Professionals Department of Information and Communication Technology Bangladesh Dhaka Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

Ransomware, a form of malicious software, encrypts victim data and renders it inaccessible until a ransom is paid. Different ransomware variants employ various tactics to avoid detection during their attacks. Understanding the techniques used in ransomware development and deployment is crucial to counter these threats. This study presents a stacked ensemble-based machine learning (ML) approach for ransomware detection. The ensemble model consists of Naive Bayes (NB), Random Forest (RF), and K-Nearest Neibors (KNN) as base classifiers and Artificial Neural Network (ANN) as meta-classifiers. Additionally, Explainable AI (XAI) utilizing LIME and SHAP has been applied to gain insights into prediction outcomes and validate model correctness. The use of LIME and SHAP ensures the inter-pretability of the model, which is a crucial factor in real-world implications. The research employs a publicly available dataset and evaluates the model using multiple metrics. Experimental results reveal that the proposed model achieves an impressive accuracy of 98.57%. The model is benchmarked against existing ML techniques, demonstrating its superior performance. © 2024 IEEE.

关键词： Adversarial machine learning

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