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Deep learning-Based Modulation recognition for Imbalanced Classification 4

Deep Learning-Based Modulation Recognition for Imbalanced Cl...

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Luo, Runqing Sun, Jian Guo, Yanan Beijing Information Science and Technology University 100101 China Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China

ISBN: (纸本)9798400709449

Automatic modulation recognition (AMR) is a crucial task in intelligent receiver systems, with significant applications in both civilian and military scenarios. Deep learning-based modulation recognition algorithms have achieved high recognition accuracy and good robustness. However, the performance of DL-AMR heavily relies on a large amount of training data. When the training dataset has a limited number of samples or an imbalanced distribution across different classes, the accuracy of automatic modulation recognition for minority class samples may significantly decrease. therefore, a CNN model with a focal loss as the output layer's loss function is proposed to address the issue of decreased recognition accuracy for minority classes in the deep learning model when faced with imbalanced training data. Simulation results demonstrate that this model improves the recognition accuracy of classes with a limited number of samples. © 2023 ACM.

关键词： Deep learning

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Accelerating DNN Training with Structured data Gradient Pruning 26

Accelerating DNN Training with Structured Data Gradient Prun...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： McDanel, Bradley Dinh, Helia Magallanes, John Franklin & Marshall Coll Lancaster PA 17604 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Weight pruning is a technique to make Deep Neural Network (DNN) inference more computationally efficient by reducing the number of model parameters over the course of training. However, mast weight pruning techniques generally does not speed up DNN training and can even require more iterations to reach model convergence. In this work, we propose a novel Structured data Gradient Pruning (SDGP) method that can speed up training without impacting model convergence. this approach enforces a specific sparsity structure, where only N out of every M elements in a matrix can be nonzero, making it amenable to hardware acceleration. Modern accelerators such as the Nvidia A100 GPU support this type of structured sparsity for 2 nonzeros per 4 elements in a reduction. Assuming hardware support for 2:4 sparsity, our approach can achieve a 15-25% reduction in total training time without significant impact to performance. Source code and pre-trained models are available at https://***/BiradMcDanel/sdgp.

关键词： Training Deep learning Computational modeling Source coding Neural networks Graphics processing units data models

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theoretical Evaluation of Transfer learning Approaches for the Identification of Lung Respiratory Sounds 4

Theoretical Evaluation of Transfer Learning Approaches for t...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Dubey, Prarthna Tripathi, Paritosh Dr. Rammanohar Lohia Avadh University Institute of Engineering and Technology Department of Information and Technology U.P. Ayodhya India

ISBN: (纸本)9798350300093

machine learning approaches for lung respiratory sound identification and categorization have increased in study. Transfer learning uses pre-trained models to extract relevant attributes from input data, one of the most promising strategies for high accuracy in this activity. In this review, many transfer learning algorithms for lung breathing sound identification are described. First, discuss respiratory sounds and their clinical significance. Next discuss the challenges of recognizing these sounds, such as the vast variety of sound characteristics and the requirement for trustworthy feature extraction techniques. Next, examine transfer learning methods such fine-tuning pre-trained convolutional neural networks (CNNs), learning from comparable tasks, and unsupervised pre-training. Focusing on current research on lung respiratory sound identification. Also compare each method's benefits and limitations. the discussion finishes with the field's most urgent difficulties, including the need for bigger and more diversified datasets, enhanced feature extraction techniques, and clinical information in machine learning models. In conclusion, this research examines the latest transfer learning algorithms for lung breathing sound recognition. It emphasizes these technologies potential to improve clinical diagnostic accuracy and efficiency and recommends numerous interesting research opportunities. © 2023 IEEE.

关键词： Feature extraction

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Elevated learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance 5

Elevated Learning based Secured Phishing Website Identificat...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Ramkumar, G. Saveetha University Department of Ece Chennai India

ISBN: (纸本)9798350379945

Phishing attacks pose a huge threat for online security and therefore need advanced detecting techniques in order to mitigate the damages it causes. this paper introduces a sophisticated model for the detection of phishing websites, named the Blended ResNet-EfficientNet Model (BREM), which unifies the advantages of the ResNet and EfficientNet architectures. To address this challenge, BREM uses the rich hierarchical pattern recognition ability of ResNet-50 and the practical feature extraction capability of EfficientNet-B3 to achieve classification performance in phishing detection. On overall assessment BREM outperforms both traditional machine learning models and standalone deep learning model with an accuracy of 96%, precision at 94%, recall of 95% and FI score of 94.5%. these same authors validate the high specificity (97 %), negative predictive value (95 %) and Matthews correlation coefficient of 0.92 further underlining the robustness and reliability of BREM. this approach does not only improve the accuracy of detection, but also provides much better security against phishing campaigns. In the near future research directions such as real-time deployment, more experiments on different feature sets, adversarial robustness, transfer learning from heterogeneous datasets, model learning models and pattern recognition. they are able to draw from an immense amount of data and address minor deviances, which allows AI's better discrimination of real versus false websites, therefore increasing user cyber protections and preventable breaches [3] [4]. At the heart of securing phishing websites identification is making use of powerful AI capabilities to scan and recognize phishing convincingly. this AI utilizes different methods - natural language processing (NLP), image recognition and behavioral analysis - to keep an eye on things happening to web content and its design. Behavior analysis tracks user-interactions and web behavior to detect for abnormal operations, which co

关键词： Phishing

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Towards Robust Policy: Enhancing Offline Reinforcement learning with Adversarial Attacks and Defenses 4th

Towards Robust Policy: Enhancing Offline Reinforcement Learn...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Nguyen, thanh Luu, Tung M. Ton, Tri Yoo, Chang D. Daejeon Korea Republic of

ISBN: (纸本)9789819787043

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling direct deployment or fine-tuning in real-world environments. However, this training paradigm can compromise policy robustness, leading to degraded performance in practical conditions due to observation perturbations or intentional attacks. While adversarial attacks and defenses have been extensively studied in deep learning, their application in offline RL is limited. this paper proposes a framework to enhance the robustness of offline RL models by leveraging advanced adversarial attacks and defenses. the framework attacks the actor and critic components by perturbing observations during training and using adversarial defenses as regularization to enhance the learned policy. Four attacks and two defenses are introduced and evaluated on the D4RL benchmark. the results show the vulnerability of both the actor and critic to attacks and the effectiveness of the defenses in improving policy robustness. this framework holds promise for enhancing the reliability of offline RL models in practical scenarios. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Intelligent Household Waste Classification System Based on machine learning 4

Intelligent Household Waste Classification System Based on M...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Linqiang, You Sindiramutty, Siva Raja A L Ashraf, Humaira Muzammal, Syeda Mariam Balakrishnan, Sumathi A P Gupta, Sarita Kavita School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Department of Computer Science and Engineering PunjabJhanjeri Mohali140307 India

ISBN: (纸本)9798350353266

In today's world, rapid global development has improved living standards but also led to a significant rise in waste generation, posing challenges for waste management systems. To address this issue, integrating smart technologies into waste classification has emerged as a promising solution. Countries like Japan, Singapore, Stockholm, and China have explored diverse models using IoT and big data to promote recycling and minimize waste. this research is dedicated to the design and development of an intelligent household waste classification system, leveraging the power of machine learning techniques. the primary objective of this system is to achieve high-precision waste detection and recognition, enabling automatic identification and categorization of diverse types of waste. By harnessing the potential of deep learning algorithms, the system aims to enhance the efficiency of waste management, mitigate environmental pollution, and foster sustainable development. the study delves into existing image recognition methodologies, explores the applicability of these techniques to waste detection and recognition, and proposes a comprehensive approach for implementing waste detection and classification. the research utilizes data from open-source repositories and supplements it with a broad collection of waste images to construct a diverse and representative dataset. the performance of two primary models, VGG13 and YOLO, is assessed in the context of waste classification. the outcomes of this research could potentially offer a practical and efficient solution for waste classification, contributing to improved waste management practices and environmental preservation. © 2024 IEEE.

关键词： component formatting insert style styling

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Efficient Unsupervised learning for Plankton Images 26

Efficient Unsupervised Learning for Plankton Images

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Alfano, Paolo Didier Rando, Marco Letizia, Marco Odone, Francesca Rosasco, Lorenzo Pastore, Vito Paolo Univ Genoa MaLGa DIBRIS Genoa Italy INFN Sez Genova Genoa Italy Italian Inst Technol Genoa Italy MIT CBMM 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. the adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. the proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties.

关键词： Image segmentation Microorganisms Pipelines Neural networks Sociology Computer architecture Feature extraction

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Understanding User Perception of Biometric Privacy in the Era of Generative AI 4

Understanding User Perception of Biometric Privacy in the Er...

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4th international conference on Communication, Computing and Industry 6.0, C216 2023

作者： Srinivasan, Srihari Cambridge International AS & A level GEMS New Millennium School - Al Khail Dubai United Arab Emirates

ISBN: (纸本)9798350327328

In the dynamic world of technology, the fusion of biometrics and artificial intelligence has ushered in an unprecedented era marked by convenience, security, and cutting-edge innovation. Biometrics, the science of using unique physical or behavioral attributes for identification and authentication, has seamlessly merged into daily routines. From unlocking smartphones with a fingerprint to clearing airport security with facial recognition, biometrics has transformed the way people interact with technology and the world. Simultaneously, Generative AI, powered by machine learning models, has unlocked new frontiers in generating remarkably realistic synthetic data, including human faces, voices, and fingerprints. this convergence of biometrics and Generative AI lies at the heart of a complex and rapidly evolving landscape, giving rise to profound questions concerning individual privacy and security. this study examines the correlation between demographic factors like age, gender, educational background, technological competence, and the regularity of employing biometric authentication, and their awareness about biometric technologies. Additionally, this research explores concerns regarding the potential misuse of biometric data and the notion that organizations should seek explicit consent before collecting such data. Lastly, it assesses the awareness of potential privacy risks and the belief that individuals should receive education regarding the utilization of their biometric data in AI systems. Descriptive research design has been used in this study. the first section of the questionnaire using Microsoft Forms covers the demographic factors, technological proficiency and frequency of using biometric authentication (e.g., fingerprint, facial recognition). the next section focuses on awareness and usage of biometric technologies, biometric privacy, trust, education, awareness of biometrics, generative AI and deepfakes using the Likert Scale. 53 samples were obtained th

关键词： Biometrics

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Methodological Implementation for Predicting Student Performance Using data mining Classifiers and machine learning 4

Methodological Implementation for Predicting Student Perform...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Mini Agarwal, Bharat Bhushan IFTM University Moradabad India

ISBN: (纸本)9798350306118

Each engineering university's major goal is to provide students with a world-class education. However, following COVID-19, when schools reopened, there was a large announcement that pupil overall performance was deteriorating daily because everything had changed from online to offline. Predicting negative grades among college students at the beginning level is critical so that their professors and management can assist and encourage them to change the negative grade into a top grade. My research area comes and provides strategies to reduce academic troubles among college students. the key goal of this research study is to become acquainted with the many aspects that aid in forecasting learner success. the second purpose is to examine and analyze the various facts. data mining classifiers and Artificial Neural Networks on a dataset including 689 student records of IP University for prediction of student performance in Python programming language. this work then entails calculating the Accuracy, Fl-score, Recall value, Precision value, ROC curve, and the Confusion matrix of several algorithms and determining the best set of rules based on the following metric scores. A Random Forest was executed with 98% accuracy. © 2023 IEEE.

关键词： Students

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A Comprehensive data Driven Approach on Crop Yield and Fertilizer Efficiency 4

A Comprehensive Data Driven Approach on Crop Yield and Ferti...

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4th international conference for Emerging Technology, INCET 2023

作者： Kesarapu, Kiran Kiran, Nelluru Sai Dhara, Erothi Manju Rupa, Reddymasu Chhabra, Gurpreet Singh Dept of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9798350335750

India's economy, which is mostly dependent on agricultural production growth and agroindustry goods, is an agricultural nation. A significant field of research for agricultural production analysis is data mining. Every farmer wants to know how much harvest he may anticipate. Examine a number of relevant factors, such as the location and the pH level used to calculate the soil's alkalinity. Moreover, the proportion of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). Location is utilized in conjunction with the usage of third-party apps like APIs to identify factors such as weather and temperature, soil type, nutrient value, the quantity of rainfall, and soil composition. All of these parameters will be reviewed, and the data will be trained to develop a model using several efficient machine-learning techniques. the system incorporates a model to give the user precise recommendations regarding the right fertilizer ratio based on field atmospheric and soil data, which improves crop output and increases farmer revenue. © 2023 IEEE.

关键词： Decision trees

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