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Machine Learning-Driven Packet Loss Classification via TCP Jersey and Multi-Layer Perceptron

Machine Learning-Driven Packet Loss Classification via TCP J...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Kiruthiga, R. Nithya, B. National Institute of Technology Dept. of Computer Science and Engineering Tiruchirapalli India

ISBN: (纸本)9798350313796

With the huge increase in the amount of data that is transferred every second, congestion in the network becomes unavoidable. There is always a need for algorithms to control the congestion in the network when the data flow increases drastically. The traditional Transmission Control Protocol (TCP) uses a slow start and congestion avoidance mechanism that is not capable of differentiating the cause of packet loss. In a heterogeneous network, the packet loss could result either from congestion in the wired network or a transmission error in the wireless network. In this paper, we propose a machine learning based improvement over TCP-Jersey, a variant of TCP that can distinguish the cause of packet loss and handle it appropriately. TCP-Jersey contains two key components, the Available Bandwidth Estimation (ABE) algorithm and Congestion Warning (CW) router configuration. Our machine learning model learns to distinguish wireless packet loss from congestion packet loss using Multi-Layer Perceptron (MLP). It predicts the cause of the packet loss and handles the congestion using bandwidth estimation algorithm. The simulation results show that our algorithm outperforms TCP-Jersey, TCP-Reno, TCP-Tahoe and TCP-Vegas in terms of throughput, goodput, fairness index and friendliness index. © 2023 IEEE.

关键词： Packet loss

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Trajectory Design and Resource Allocation for UAV-Assisted Computation Offloading 7

Trajectory Design and Resource Allocation for UAV-Assisted C...

引用

7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Yunxin Wang, Jiale Wang, Junhao Que, Xingle Lu, Dandan School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Engineering Technology Center Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

This paper studies air-ground cooperative communication based on unmanned aerial vehicles (UAVs) and device-to-device (D2D) technology. This field has become a research hotspot due to its susceptibility to passive eavesdropping and privacy leakage. To enhance data transmission security, this paper proposes a full-duplex UAV-assisted scheme to address potential ground eavesdropping risks in UAV-assisted computation offloading tasks. The scheme transmits interference signals while receiving computation tasks to counteract eavesdroppers. Given the limited energy of UAVs and the inadequacies of existing energy optimization research, this paper establishes a UAV-assisted mobile edge computing (MEC) communication system model. To address the non-convex optimization problem within the model, a successive convex approximation algorithm is employed for simplification and decomposition. User scheduling, power allocation, and UAV trajectory design subproblems are solved through an alternating iteration method. Simulation results validate that the proposed method can effectively improve the system's secure communication performance even in the presence of uncertain eavesdropper locations. ©2024 IEEE.

关键词： Convex optimization

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Improving Liver Disease Diagnosis Through Generative Adversarial Network Driven Synthetic Tabular Data

Improving Liver Disease Diagnosis Through Generative Adversa...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Mahendran, S. Venkatasekhar, D. Shanmugasundaram, G. Annamalai University Department of Information Technology Chidambaram India Chennai Institute of Technology Dept. of Computer Science & Engineering Chennai India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540821

Liver ailments pose a significant worldwide health concern, with their extensive effects and severe repercussions highlighting the pressing necessity for precise diagnosis. Diagnosing hepatic disorders is frequently intricate and expensive. This work presents a Generative Adversarial Network (GAN) based methodology for producing synthetic tabular data to enhance early prediction of liver ailments, employing a dataset of Indian liver patients. The contribution of the work is the introduction of innovative architecture that integrates Stacked Ensemble Model with advanced preprocessing techniques and the use of Generative Adversarial Networks for data augmentation not extensively explored in current data-driven machine learning approaches for liver ailments prediction. The Stacked Ensemble Model has two tiers: the base-level and the meta-level. The base-level learning component trains various base learners, including Gradient Boosting, Ride classifier, Multi-layer Perceptron, and LightGBM on the input dataset. A strong prediction model is then constructed by combining and feeding the outputs of these several classifiers into a Random Forest classifier, which is the meta-learner. The results show that the model performs much better when synthetic data generated by GANs is included, obtaining an overall accuracy of 95%. This approach aims to greatly improve the prediction of liver ailments, hence enabling quick intervention and more effective treatment. © 2024 IEEE.

关键词： Generative adversarial networks

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Deepfake Detection Using Multi-Modal Fusion Combined with Attention Mechanism 4

Deepfake Detection Using Multi-Modal Fusion Combined with At...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkatesan, R. Yashvanth Ram, R.V. Elango, Elakkiya Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Government Arts College for Woman Dept. of computer Science Sivaganga India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540364

The proliferation of deepfake technology poses a significant challenge to the authenticity of digital content. This research explores the application of multimodal fusion techniques to enhance deepfake detection accuracy. By combining visual and audio features, the proposed method leverages the complementary nature of different data types to detect discrepancies introduced by deepfake manipulation. An attention mechanism is incorporated to focus on salient regions within each modality, further improving detection accuracy. Convolutional Neural Networks (CNNs) and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction, followed by feature fusion for deepfake detection. This approach demonstrates the effectiveness of multimodal fusion in combating the evolving threat of deepfake technology. By advancing deepfake detection techniques, this research contributes to safeguarding the integrity of digital content and preserving trust in media. © 2024 IEEE.

关键词： Convolutional neural networks

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Development of an Ensemble Model for Disease Prediction using Laboratory Tests 7

Development of an Ensemble Model for Disease Prediction usin...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rao, V. Chandra Shekhar Sujatha, M. Navitha, Jalagam Karthikeya, Chirra Goutham, Bheemandla Jairam, Puvati Kakatiya Institute of Technology & Science Dept of Cse Warangal India Kakatiya Institute of Technology & Science Computer Science And Engineering Warangal India

ISBN: (纸本)9798350359299

The Machine learning model learns from given input data and make decisions or predictions without requiring any type of explicit programming. Machine learning models can observe trends, gain knowledge from past mistakes, and gradually enhance their performance. The utilization of machine learning is increasing in daily life especially in medical field for diagnosing a disease. The incorporation of machine learning into the quickly changing healthcare landscape has become increasingly pivotal for enhancing diagnostic accuracy and personalized patient care. The study is all about the preparation of an ensemble model that is made for predicting various diseases, leveraging insights gleaned from a diverse array of laboratory tests. Three potent models, including 'random forest, extreme gradient boost, and light gradient boost, are included in the ensemble model. The research highlights how machine learning is influencing daily experiences with healthcare in a larger way in addition to concentrating on the creation and assessment of an accurate disease prediction. The ensemble model developed intends to showcase the capabilities of machine learning in handling complex medical data and delivering reliable predictions. © 2024 IEEE.

关键词： Forecasting

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Active Element Arrangement and Prediction Model for Channel Estimation in IRS-Assisted Systems

Active Element Arrangement and Prediction Model for Channel ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Tsuchiya, Yoshihiko Suga, Norisato Uruma, Kazunori Fujisawa, Masaya Tokyo University of Science Dept. of Information and Computer Technology Tokyo Japan Shibaura Institute of Technology Dept. of Information and Communication Engineering Tokyo Japan Kogakuin University Dept. of Computer Science Tokyo Japan

ISBN: (纸本)9798350350821

Intelligent reflecting surfaces (IRS) that can dynamically control the phase of radio waves and reflect them are attracting attention to realize non line-of-sight communication in the high-frequency band. Channel state information (CSI) is estimated to properly control the phase, however the overhead of the estimation reduces communication efficiency. To reduce the overhead, IRS systems with active elements that perform channel estimation are considered. One approach treats the partial CSI obtained from the active elements as an image and utilizes a super-resolution technique to predict the entire CSI. However, because the active elements are arranged in grid, prediction is difficult when the active elements spacing and the CSI fluctuation period match. In this study, we propose a new arrangement that gathers the active elements in the center of the IRS. Furthermore, we regard prediction as an image-inpainting problem that recovers missing pixels from known pixel values, and we propose applying the U-Net to solve this problem. Numerical experiments show that the proposed method outperforms conventional methods in prediction accuracy and spectral efficiency. © 2024 IEEE.

关键词： Channel estimation

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Comparative Study of Convolutional Neural Network Object Detection Algorithms for Image Processing

Comparative Study of Convolutional Neural Network Object Det...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Reddy, Saieshan Pillay, Nelendran Singh, Navin University of Technology Dept. of Electronic & Computer Engineering Durban South Africa

ISBN: (纸本)9798350327816

This paper presents a comparative study on three Convolutional Neural Network (CNN) object detection algorithms to find the best detector based on the combination of speed and accuracy on a personal computer. The MATLAB® development environment is used to evaluate three different object detector algorithms, namely Faster Region-Based Convolutional Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). These algorithms are trained, and their performance metrics are tested on a small sample dataset. The results show that the SSD object detector algorithm performs best when considering both performance and processing speeds. Faster R-CNN detected objects at an average speed of 4.838 seconds and achieved a mean average precision of 0.76 with an average loss of 0.429. SSD detected objects at an average speed of 0.377 seconds and achieved a mean average precision of 0.92 with an average loss of 1.754. YOLO v3 detected objects at an average speed of 1.004 seconds and achieved a mean average precision of 0.81 with an average loss of 2.739. © 2023 IEEE.

关键词： computer Vision Convolutional Neural Network Image Processing MATLAB Object Detection

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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

引用

2024 IEEE International Conference on Emerging Technologies in computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Sunanda, H.G. Kavitha, K.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru560004 India Visvesvaraya Technological University Belagavi590018 India Dayananda Sagar College of Engineering Dept. of Computer Science and Engineering Bengaluru560078 India

ISBN: (纸本)9798350372502

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare. © 2024 IEEE.

关键词： Motion estimation

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Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali Language Comment on Facebook 2

Hybrid CNN LSTM Approach for Sentiment Analysis of Bengali L...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Nobel, SM Nuruzzaman Swapno, S M Masfequier Rahman Ramachandra, A.C. Shajeeb, Hasibul Hossain Islam, Md Babul Haque, Rezaul Bangladesh University of Business and Technology Dept of Computer Science and Engineering Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh

ISBN: (纸本)9798350317558

This study addresses the challenge of sentiment analysis in Bengali Facebook comments, where users express their opinions on various topics. Existing sentiment analysis techniques have difficulty achieving high accuracy in this situation, which has inspired the creation of a fresh methodology. To improve sentiment analysis of Bengali Facebook comments, we suggest a model that combines a hybrid convolutional neural network (CNN) and a long short-term memory network (LSTM). Our research adds to the expanding area of sentiment analysis, especially for languages with few resources, such as Bengali. The success of the Hybrid CNN LSTM model opens the door for more precise and context-aware sentiment analysis on social media platforms, allowing for a better understanding of user sentiments and boosting user experiences. Our study focused on dividing comments into five groups according to their emotional content: troll, not bully, sexual, religious, and threat. We carefully generated the handmade dataset and ensured the quality and relevance of a dataset of 50,000 Bengali comments before using it to train and test our algorithm. The Hybrid CNN LSTM model outperforms conventional approaches and achieves an outstanding accuracy rate of 92.09%, demonstrating amazing efficiency in sentiment recognition. This dramatic increase in precision indicates that machine learning techniques are successful in sentiment analysis of Bengali Facebook comments. © 2024 IEEE.

关键词： Bengali Language CNN Comment Facebook Fully Connected Layer Hybrid LSTM NLP Predict Sentiment

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Advancements in EEG-Based Emotion and Consciousness Recognition: Techniques, Challenges, and Innovations 1

Advancements in EEG-Based Emotion and Consciousness Recognit...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Jadekar, Ranjana B. Poornima, B. Gowramma, B.H. Puneeth, S.P. Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Karnataka Belagavi India Visvesvaraya Technological University Bapuji Institute of Engineering and Technology Dept. of Information Science and Engineering Karnataka Belagavi India

ISBN: (纸本)9798350376517

This study delves into the application of Electroencephalogram (EEG)-based techniques for emotion and consciousness recognition, focusing on disorders of consciousness (DOC). By evaluating existing approaches in terms of techniques, methodologies, and accuracies, the research highlights the efficacy of both machine learning and deep learning models. The key challenges were identified such as noise removal due to artifacts and variability in patient responses. The findings underscore the potential of advanced computational methods in enhancing the reliability and precision of EEG-based diagnostic tools, paving the way for better clinical interventions and deeper understanding of human emotions. Also, the findings show that using deep learning, the current works have achieved better results for identification of emotion. © 2024 IEEE.

关键词： Electroencephalography

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