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Analysis of Prediction of Pneumonia from Chest X-ray images using CNN and Transfer Learning 5

Analysis of Prediction of Pneumonia from Chest X-ray images ...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Vinay, K. Shashanka Kodipalli, Ashwini Swetha, P. Kumaraswamy, S. Dept. of Computer Science and Engineering Global Academy of Technology Karnataka Bengaluru India Dept of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bengaluru India

ISBN: (纸本)9798350361155

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment;rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawback

关键词： Convolutional neural networks

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Internet of Things IoMT data Aggregation Using Machine Learning 2

Internet of Things IoMT Data Aggregation Using Machine Learn...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Pulabaigari, Lakshmi Sagar Sivakumar, C. School of Computing Mohan Babu University Dept. of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Mohan Babu University Dept. of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350372847

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available. © 2024 IEEE.

关键词： Internet of things

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Cross-Layer Framework for IoT data Protection Using Blockchain Based Federated Learning Model (BCFL) 4

Cross-Layer Framework for IoT Data Protection Using Blockcha...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Murali, Srividhya B Raja, S.R. Kanimozhi, E. Akila, D. Khan, Abdul Azeem Alajmani, Samah H. SRM Institute of Science and Technology Dept. of Computer Applications Kattankulatur Chennai India Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Dept.of CS Chennai India Shri Shankarlal Sundarbai Shasun Jain College Dept. of Computer Applications Chennai India Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Dept. of Computer Applications Chennai India Faculty of Islamic Technology Brunei College of IT Taif University Department of IT Saudi Arabia

ISBN: (纸本)9798350353266

In recent years, concerns about the security of data exchanged between the Internet of Things (IoT) devices have increased with respect to information security. data security and the prevention of critical information leaks are both facilitated by a security model. Using Blockchain technology, the issues with data security in IoT devices may be resolved. Decentralized ledger technology known as blockchain enables many useful applications, including distributed storage, consensus, encryption, and Machine-to-Machine (M2M) data transmission. This paper develops a cross-layer framework for protecting the sensitive data of IoT by applying Blockchain based Federated Learning model (BCFL). The data records are organized into blocks in the BCFL paradigm, and then encode and decode methods are used to send the data to the servers. Using the Federated learning method, the block size and coding redundancy factor are calculated in a block-by-block transmission manner. To protect the integrity of data, a hash function and a digital signature are computed for each block of data. By experimental results, it was shown that the proposed BCFL model attains higher detection accuracy and correctness of data with reduced encoding/decoding time and reconstruction time. © 2024 IEEE.

关键词： Federated learning

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Wormhole Attack Detection in the Internet of Things using a Hybrid Deep Learning Model 3

Wormhole Attack Detection in the Internet of Things using a ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Geo Francis, E. Sheeja, S. Antony John, E.F. Joseph, Jismy Dept. of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Dept. of Data Science Sri Krishna Adithya College of Arts and Science Tamil Nadu Coimbatore India Kerala Thrissur India Kerala Mala India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Internet growth has led to widespread adoption and transmission of the IoT in numerous areas. From personal networks to industrial applications, healthcare to transportation, IoT is everywhere. Due to their widespread usage, attackers deploy sophisticated methods and software to infiltrate IoT networks. IoT devices' low processing power and resources can threaten security and privacy. This vulnerability has inspired attackers to undertake a variety of attacks against IoT networks. Wormhole attacks (WHA) are routing attacks which can interrupt IoT network packet routing and flow. To address the security issues related to IoT devices, a novel hybrid deep Learning (DL) method is proposed to classify potential security risks in IoT circumstances. The proposed module combines the Bidirectional Long Short-Term Memory with attention mechanism (ABS-BiLSTM) and AlexNet CNN to achieve higher detection accuracy. The security service provided by the proposed monitoring solution ensures compatibility among various IoT network communication protocols. The results clearly show that the effectiveness and excellence of the schemes in terms of network performance, accuracy, and detection speed enhancement. © 2025 IEEE.

关键词： Network intrusion

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Heart Abnormality Detection Through Neural Network

Heart Abnormality Detection Through Neural Network

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Sarada, B. Smitha Chowdary, Ch. Babu, Jampani Satish Kavitha, K. Dept. of Data Science Secunderabad India Koneru Lakshmiah Education Foundation Dept. of Cse Guntur India P.B.Siddhartha College of Arts and Science Dept. of Computer Science Vijayawada India

ISBN: (纸本)9798350300857

This research study aims to develop an algorithm using phonocardiograms and Deep learning to differentiate between normal and abnormal heart sounds. The increase in cardiovascular diseases (CVD's), primarily caused by unhealthy habits, necessitates efficient diagnosis. The study utilized 1055 data samples, with 833 being normal and 182 abnormal, each lasting 5 seconds. Logistic regression and a three-layered neural network were initially applied, yielding accuracies of 42% and 63%, respectively. By extracting features using spectral roll-off and reapplying the models, accuracies improved to 79% and 86%. Up sampling the data further increased the accuracies to 86% for logistic regression and 89% for the three-layered neural network. The findings demonstrate the potential of audio-based algorithms in diagnosing cardiovascular diseases accurately. © 2023 IEEE.

关键词： Multilayer neural networks

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Land Cover Clustering for Change Detection using Deep Belief Network

Land Cover Clustering for Change Detection using Deep Belief...

引用

2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Sivasankari, A. Jayalakshmi, S. Vistas Dept. of Computer Science Chennai600117 India Vistas Dept. of Computer Applications Chennai600117 India

ISBN: (纸本)9781665484251

Change detection in recent era plays a major role in the remote sensing areas that includes land cover and water bodies. The application of change detection in remote sensing is considered complicated due to continuous interaction of humans with nature. Further, the change detection in land cover and water bodies plays a major role in global change. Manual change detection process via experts consumes more time to survey the land, whereas detection of changes in land cover and water bodies via an artificial intelligence (AI) model enables faster change detection. However, multi-sensor and multi-temporal satellite data calibration poses a serious problem in remote-sensing application. It makes the entire clustering process a difficult one and hence the accuracy of change detection degrades. In this paper, we apply the task of unsupervised learning to a deep learning model namely deep belief network. Here, the land cover images are clustered using the unlabeled data, where the algorithms are trained in unsupervised manner. The intelligent decisions are made available via deep belief network that clusters the uncategorized the land covers and water bodies images from the satellite image database. The dataset includes Landsat sensors' datasets in dry semi-arid river basin in Western India. The performance of the unsupervised deep belief network is evaluated in terms of various performance metrics that includes accuracy, precision, recall, f-measure, geometric mean, peak signal noise ratio and mean average percentage error. © 2022 IEEE.

关键词： Change detection

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Forecasting Traffic Air Pollution Increases from Urban Transportation Using Machine Learning in Metropolitan Cities-A Review

Forecasting Traffic Air Pollution Increases from Urban Trans...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Hari Naga Vijaya Bhaskar V. Valli Mayil Dept. of Computer Science and Applications KL University.

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5–10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. This review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements.

关键词： Deep learning Reviews Atmospheric modeling Computational modeling Transportation Predictive models Air pollution data models Forecasting Public healthcare

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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