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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Advanced Source Privacy: DQ-NN and Phantom Routing in IoT-Integrated Multiple-Source and Destination Wireless Sensor Networks 15

Advanced Source Privacy: DQ-NN and Phantom Routing in IoT-In...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. University Visvesvaraya College of Engineering Bangalore University Department of Computer Science and Engineering Bengaluru560001 India Manipal Institute of Technology Department of Information Technology Bangalore India Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350370249

Internet of Things (IoT) and Wireless Sensor Networks (WSNs) have brought about revolutionary opportunities for many areas, they have also raised serious security issues, especially with relation to source node location privacy in IoT-enabled WSNs. In response, we put out a brand-new hybrid Deep Q-learning Neural Network (DQ-NN) strategy designed specifically for Source Location Privacy (SLP) in Internet of Things (IoT)-enabled WSNs that use phantom routing. Through the strategic selection of phantom nodes, our method establishes various routing paths from source to sink nodes via these phantoms, taking into account factors such as neighbour lists, energy levels, distances, and trust heterogeneity. The use of DQ-NN, a combination of Deep Q-learning Network (DQN) and Deep Neural Network (DNN), is essential to our approach since it guarantees source node location privacy protection in addition to effective and reliable data transfer through IoT-enabled WSNs, strengthening the privacy and security environment of IoT networks and facilitating easy integration with everyday life. The suggested DQ-NN performs better than other current methods. © 2024 IEEE.

关键词： deep q learning neural network deep q neural networks Internet of things phantom routing source location privacy

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Adaptive Beamforming in MIMO Systems Using Deep learning

Adaptive Beamforming in MIMO Systems Using Deep Learning

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2024 IEEE international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Vinithra Banu, T. Hussein, Layth Grover, Varsha Kumar, T. Arun Sharma, Rupesh Sudha, I. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Kongu Engineering College Department of Cse India Ies University Ies Institute of Technology and Management Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

One of the most significant procedures in MIMO system is the adaptive beamforming which is the technique of adjusting the direction of signal transmission for better wireless communication. This paper aims to investigate the application of deep learning in combination with adaptive beamforming for the improvement of the existing technologies and techniques having some limitations which prove efficient when working in certain applications and scenarios only. Utilizing the CNNs and RNNs, the proposed approach then determines ideal beamforming patterns from various datasets, synthetic simulations, as well as measured ones. In this paper, various approaches are employed which include the Super & Reinforcement learning techniques, which makes the deep learning-based models capable of learning in real-time the change of channel characteristics. Simulations and practical adaptations of changing protocols have illustrated substantial enhancements on SINR, BER, and system capacity. The conclusion indicates that the proposed DL based technique is better than the existing standards providing more considerably higher data rates with reasonable less error rates and possessing highly adaptive capability. From the literature, it is clear that deep learning has a significant use in adaptive beam forming;although it has significantly increased the computational load. For future wireless communication technologies such as 5G and beyond MIMO systems this work has demonstrated the promises of deep learning to improve such systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Deep learning Model for Securing KMS Through CSF Deviation Detection 13

Intelligent Deep Learning Model for Securing KMS Through CSF...

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Chauhan, Raj Ahmad, Shahnawaz Onyarin, Ogala Justin Kumar, Rajendra Noida Institute of Engineering and Technology Department of CSE-AIML Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India University of Delta Cyber Security Department Delta State Nigeria Jamia Millia Islamia Department of Computer Science New Delhi India

ISBN: (纸本)9798350380583

Cloud computing has revolutionized data content stock and administration but concurrently it has increased the vulnerability factors especially Key Management System (KMS), which is crucial since it is used to protect cypher key. Typically, there are several limitations to anomaly detection methods used currently: poor precision and insufficient speed to detect threats. This research presents an intelligent model that integrated deep learning technique into existing Cloud Security Framework (CSF) to conduct proactive identification on the deviation from the baseline behavior of interactions between the applications and the KMS. This is achieved through continually accruing KMS log data, defining behavioral profiles for the applications and using clustering to categorize them appropriately. The model also continuously monitors the system for any deviations, making anomalies easily discernible for quick action either through raising alerts to stakeholders or when using the KMS API to automatically respond to the flagged issues. Studies in the multitenant cloud scenario indicate that implementation of LSTMbased model is more effective than conventional approaches, enhancing the CSF's capability in threat detection thus making it possible to quickly mount defensive measures to safeguard crucial data and cryptographic keys. © 2024 IEEE.

关键词： Cloud security

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Improving the Efficiency of Production Processes by Reducing Human Errors Using Intelligent Methods 19th

Improving the Efficiency of Production Processes by Reduc...

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Musial, Kamil Balashov, Artem Burduk, Anna Sulowski, Robert Pihnastyi, Oleh Wroclaw University of Science and Technology 27 Wybrzeże Stanislawa Wyspiańskiego Street Wroclaw50-370 Poland Brose Sitech Sp. z o.o. 2 Strefowa Street Polkowice59-101 Poland National Technical University "Kharkiv Polytechnic Institute" 2 Kyrpychova Street Kharkiv61002 Ukraine

ISBN: (纸本)9783031750120

This paper addresses the critical challenge of human resource management in production processes, aiming to predict and mitigate human errors that significantly impact production costs. The study introduces a novel hybrid approach combining machine learning algorithms-K-Nearest Neighbors (KNN), Decision Tree, and Neural Networks-to forecast the occurrence of human errors effectively. Comprehensive production data was collected and analyzed, with each model’s predictive accuracy and effectiveness evaluated. The KNN algorithm demonstrated simplicity and effectiveness in pattern recognition, while Decision Trees provided clear decision rules and Neural Networks excelled in handling complex, non-linear relationships. The results reveal that integrating these models into production management systems can enhance operational efficiency and reduce costs by better anticipating and mitigating human errors. This research offers valuable insights into optimizing human resource allocation and improving overall production effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Agricultural AI Insights: Deciphering Jute Leaf Diseases Through Federated learning CNNs 10

Agricultural AI Insights: Deciphering Jute Leaf Diseases Thr...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Rajput, Kapil Garg, Ashish Bhati, Jai Prakash Kukreja, Vinay Mehta, Shiva Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Iimt College of Engineering Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350384369

This paper discusses using FL with CNN to detect and categorize five jute leaf diseases. Applying datasets from five disparate clients, each representing a different ecological and geographical zone of jute farming, the study observes whether this approach effectively mitigates disease detection challenges challenging data privacy and integrity. The methodology adopted utilizes the efficacy of CNN to extract features and classify them, complemented by FL's decentralized collaborative approach. For evaluation purposes, metrics such as precision, recall, F1-score support, and accuracy were utilized within all clients' disease classes. The findings depict a stepwise increase in detection efficiencies shown by the macro, weighted, and micro averages. For example, client OB-1 has a baseline performance with macro, weighted, and micro averages of 79.68%, 80.13%, and 80. However, client OB-5 peaked, with these averages reaching 94.27%, 94.28%, and 94. The presented study shows that the local model updates, combined with the global one via aggregation algorithms, give a highly accurate model with the highest recorded accuracy on ob-5 - 0.98 and are also robust to different datasets. Finally, the study verifies the viability of using federated learning and CNN in rural environments, especially for classifying jute leaf disease. The results show that the model can learn from far-reaching data sources and is a promising tool to improve crop health monitoring systems globally. © 2024 IEEE.

关键词： Federated learning

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A Framework for Adopting machine learning in the Clinical Domain 2

A Framework for Adopting Machine Learning in the Clinical Do...

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2nd international conference on data, Information and computing science, CDICS 2024

作者： Tan, Esther Meng-Yoke Liu, Fan Chia, Yuen Kwan National University of Singapore Singapore

ISBN: (纸本)9781643685762

The usage of machine learning in the clinical domain enables better decision-making skills and improves the quality of health care services rendered to the patients. This paper described a new MLOps framework which addresses the challenges faced when applying AI in the clinical domain. These include the handling of heterogenous, incomplete and evolving medical data sets, ensuring privacy of patients, controlling the versions of dataset and models adopted. The proposed framework supports a repository of evolving anonymized patient medical data, tracking of the feature selection and models adopted using versioning control system and records the accuracy of their outcomes. It also enables audit tracking and ensures transparency and accountability, thus helping to increase medical workers' confidence in machine learning and encourages the use of ML in the clinical work. For evaluation, the proposed framework is set up in the clinical environment using existing patient’s records. Evidences, such as records of model training using specific datasets traceable to the actual patients’ records, are collected. These evidences fulfill the audit requirements and form the basis to justify research funding in future AI-related medical work. Finally, the study also proposes future work and directions to extend the framework into other domains. © 2025 The Authors.

关键词： Adversarial machine learning

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High-order functional connectivity based Major depressive disorder classification using self-attention based CNN 7

High-order functional connectivity based Major depressive di...

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7th international conference on data science and Information Technology, DSIT 2024

作者： Chenxi, Tian Meili, Lu Tianjin University of Technology and Education School of Information Technology Engineering Tianjin China

ISBN: (纸本)9798350384093

Major depressive disorder (MDD) is a common and socially significant psychiatric disorder with extremely complex pathologic mechanisms. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used for the classification and diagnosis of MDD. Most current studies reveal the relationship between MDD and brain function by extracting static low-order functional connectivity (SFC) between brain regions, often neglecting potential high-order interactions that may affect classification performance. Therefore, to fully exploit the more complex biomarker features between brain regions and enhance classification performance, this project extracts two types of dynamic high-order functional connectivity (dHOFC): dynamic topographic high-order functional connectivity (dtHOFC) and dynamic associative high-order functional connectivity (daHOFC). It fuses the features of these two types of dynamic high-order functional connectivity and proposes a self-attention-based CNN method for MDD classification validation. The results demonstrate that the proposed deep learning algorithm achieves high classification performance, with the fusion of dtHOFC and daHOFC features providing higher classification accuracy compared to using individual high-order functional connectivity features. © 2024 IEEE.

关键词： Deep learning

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Big data-Driven Long Term Accurate CO2 Emission Forecasting Using Advanced Recurrent Neural Networks

Big Data-Driven Long Term Accurate CO2 Emission Forecasting ...

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2024 IEEE international conference on Big data, Bigdata 2024

作者： Verma, Anil Routhu, Lalit Chandra Chigrupaatii, Rishikant Choubey, Anurag Misra, Rajiv Singh, T.N. Indian Institute of Technology Patna Dayalpur Department of Computer Science and Engineering Daulatpur801106 India Bennett University Scset Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350362480

Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation. © 2024 IEEE.

关键词： Low emission

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Predicting Stock Closing Prices: A Deep learning Approach to Financial Forecasting 1

Predicting Stock Closing Prices: A Deep Learning Approach to...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Elangovan, R. Santhi, B. Usha, J. Ranjith, B.K. School of Management SASTRA Deemed University Thanjavur613 401 India School of Arts Science and Humanities SASTRA Deemed University Thanjavur613 401 India

ISBN: (纸本)9798331518943

In an era of volatile financial markets, accurate stock price prediction is essential for informed investment decisions. This project tackles the challenges of forecasting stock closing prices for ***, ***, and *** using advanced deep learning models over a comprehensive 10-year dataset. Through innovative preprocessing methods - such as forward filling for missing values, normalization for model stability, and reshaping data into sequences for recurrent neural networks (RNNs) - the study employed three architectures: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). An ensemble approach that combined predictions from these models was explored to enhance overall prediction accuracy. However, the Gated Recurrent Unit (GRU) model consistently demonstrated superior performance, achieving the highest $\mathbf{R}^{\mathbf2024}$ score of 0.998185 and the lowest Mean Absolute Percentage Error (MAPE) of 4.68947 in dataset 1, along with strong results in dataset 3 with an $\mathbf{R}^{\mathbf2024}$ score of 0.994933. Although the LSTM model showed promise in dataset-2, it exhibited a significantly higher MAPE, indicating its limitations. The findings underscore the effectiveness of the GRU model for stock price prediction, highlighting the importance of selecting appropriate modeling techniques based on dataset characteristics while establishing a robust framework for financial forecasting using advanced deep learning methodologies. © 2024 IEEE.

关键词： Prediction models

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Visual Detection of Marine Debris Using RTMDet 7

Visual Detection of Marine Debris Using RTMDet

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7th international conference on data science and Its Applications, ICoDSA 2024

作者： Bagus Padmanawijaya, Aryya Kosala, Gamma Sthevanie, Febryanti School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350365351

Marine debris, a hazardous threat to the marine environment, has emerged as a critical global environmental issue. The commonly applied method to remove marine debris from the marine environment is manual removal. One solution to this problem is to use deep learning-based visual marine debris detectors to automatically detect marine debris. However, in terms of marine debris detection, there are several challenges, one of which is because the complex marine environment makes visual detectors able to detect marine debris accurately and in real-time so as not to damage the marine environment. In this research, three variants of the RTMDet (Real-Time Models for Object Detection) model were trained and evaluated using the TrashCan-Instance dataset. One of them is the RTMDet-1 model with improvisation to replace the loss bbox in the head model with DIoU (Distance-Intersection over Union) and improvisation to add a sampling strategy with a Class-aware Sampling technique to handle the imbalanced data problem that has obtained mAP50(Mean Average Precision at threshold 50%) accuracy of 71.3%. This has made the model's best object detection accuracy on the TrashCan-Instance dataset while maintaining detection speed. These results prove that the model proposed in this study can be a vital consideration for further development in detecting marine debris. This contribution aims to address the global challenges related to marine debris and stimulate the development of more effective and efficient object detection models in complex marine environments. © 2024 IEEE.

关键词： Deep learning

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