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Intelligent Systems for Communication, IoT and Security (ICISCoIS), International Conference on

作者： M Pyingkodi N R Wilfred Blessing K Thenmozhi K Kanimozhi L Pavithira P K Sandhya Balakrishnan Department of Computer Applications Kongu Engineering College Erode India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri(Government) Oman Department of Computer Science Kristu Jayanti College Bengaluru India Department of Information Technology Sri Krishna Adithya College of Arts and Science Coimbatore Department of Computer Science with Data Analytics PSG college of Arts & Science College Coimbatore Department of Computer Science CMS College of Science and Commerce Coimbatore

On international basis, one in all our century's essential problems is air pollutants. Every year, the range of individuals killed via way of means of pollutants climbs, with air pollutants being the main cause. The Internet of Things (IoT) is used worldwide community of “inventive gadgets” which could sense, and join to, and have interaction with different humans and systems. The solution is furnished via way of means of a sensor-primarily based totally hardware module that can be deployed anywhere. These gadgets, which can be hooked up on mild poles, homes, and different structures, wirelessly speak air pleasant facts to a far flung server. The microprocessor collects facts from IoT sensing gadgets and sends it to a cloud server for processing. These facts can be used to expecting while the vicinity is maximum polluted. The machine additionally gives air pleasant statistics through a cell application, permitting clients to choose alternatives wherein the air pleasant is great. It is likewise a totally less expensive device.

关键词： Cloud computing Microprocessors Air pollution Hardware Sensors Servers Internet of Things

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Estimating causal effects under network interference with Bayesian generalized propensity scores

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 13101-13161页

作者： Laura Forastiere Fabrizia Mealli Albert Wu Edoardo M. Airoldi Yale Institute for Network Science Yale University New Haven CT Department of Statistics Computer Science Applications University of Florence Florence Italy Department of Statistics Harvard University Cambridge MA Department of Statistics Operations and Data Science Fox School of Business Data Science Institute Temple University Philadelphia PA

Real-world systems are often comprised of interconnected units, and can be represented as networks, with nodes and edges. In a social system, for instance, individuals may have social ties and financial relationships. In these settings, when a node (the unit analysis) is exposed to a treatment, its effects may spill over to connected units; then estimating both the direct effect of the treatment and its spillover effects presents several challenges. First, assumptions about the mechanism through which spillover effects occur along the observed network are required. Second, in observational studies, where the treatment assignment has not been randomized, confounding and homophily are further potential threats to the identification and to the estimation of causal effects, on networks. Here, we make two structural assumptions: (i) neighborhood interference, which assumes interference operates only through a function of the immediate neighbors' treatments, and (ii) unconfoundedness of the individual and neighborhood treatment, which rules out the presence of unmeasured confounding variables, including those driving homophily. Under these assumptions we develop a new covariate-adjustment estimator for direct treatment and spillover effects in observational studies on networks. We proposed an estimation strategy based on a generalized propensity score that balances individual and neighborhood covariates across units under different levels of individual treatment and of exposure to neighbors' treatment. Adjustment for propensity score is performed using a penalized spline regression. Our inference strategy capitalizes on a three-step Bayesian procedure, which allows to take account for the uncertainty in the propensity score estimation, and avoids model feedback. The correlation among connected units is taken into account using a community detection algorithm, and incorporating random effects in the outcome model. All these sources of variability, including variability of

关键词： causal inference interference spillover effect Bayesian inference network data

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Jia Lina Shahab S. Banda Hao-Ting Paib Bharat S. Rawal International Graduate School of Artificial Intelligence Douliu City Yunlin Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science Digital Technologies University Ggrambling La USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL’s reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent’s decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market.

关键词： Uncertainty Correlation Additives Explainable AI Computational modeling computer architecture Deep reinforcement learning Fraud Portfolios

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Enhancing Communication Efficiency and Training Time Uniformity in Federated Learning through Multi-Branch Networks and the Oort Algorithm

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Algorithms 2024年第2期17卷 52页

作者： Juan, Pin-Hung Wu, Ja-Ling Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei106 Taiwan Center for Data Intelligence: Technologies Applications and Systems National Taiwan University Taipei106 Taiwan

In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. The experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems. © 2024 by the authors.

关键词： Efficiency

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A Secured Multiple Party Key Agreement Protocol Design Over Cloud Computing Platform by Using Statistical data Analysis Logic

A Secured Multiple Party Key Agreement Protocol Design Over ...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Allam Balaram Sajja Suneel P.M. Kavitha Achinta Saikia S. Gopi Y Dileep Kumar Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of Computer Science and Applications SRM Institute of Science And Technology Ramapuram Chennai-89 Department of Statistics Mariani College Mariani Jorhat Assam Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of ECE School of Engineering Mohan Babu University Tirupati

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In the field of cloud computing, ensuring secure and efficient key agreement among multiple parties has emerged as a paramount challenge. Traditional key agreement protocols often rely on central authorities or trusted third parties, posing significant security and privacy concerns. To address these challenges, this paper introduces a novel key agreement protocol designed specifically for cloud computing platforms, emphasizing security, efficiency, and resilience without depending on a trusted third party. The proposed protocol innovatively combines Distributed Key Generation (DKG) with a Dynamic Consensus Mechanism, Zero-Knowledge Proof (ZKP) based authentication, and a Multi-Cloud Redundancy approach, offering a comprehensive solution to secure multi-party communication in distributed cloud environments. The DKG protocol facilitates the collaborative generation of a shared secret among participants, significantly enhancing security by eliminating single points of failure. The proposed Dynamic Consensus Mechanism ensures the integrity and finality of key agreement transactions on a blockchain-based ledger, adapting to network conditions and participant trust levels to optimize performance without compromising security. ZKP-based authentication allows participants to verify their identities without revealing sensitive information, preserving privacy and thwarting impersonation attacks. Lastly, the Multi-Cloud Redundancy strategy enhances the protocol's resilience to cloud-specific vulnerabilities and service outages, ensuring high availability and robustness.

关键词： Cloud computing Privacy Redundancy Authentication Impersonation attacks Robustness Consensus protocol

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TriBoost and Beyond: Advanced Machine Learning Approaches for Diabetes Risk Prediction

TriBoost and Beyond: Advanced Machine Learning Approaches fo...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Soham Biren Katlariwala Vaibhav C. Gandhi Nirav Patel Devendra Parmar Ayushi Desai B.S. Data Science and Applications Indian Institute of Technology Madras Chennai India Department of Computer Engineering MBIT The Charutar Vidya Mandal (CVM) University Anand India Parul Institute of Technology Parul University Vadodara India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Diabetes is a tremendous world health issue and, in its management, and complications prevention, proper screening is vital. This paper examines the improvement of accurate diabetes risk prediction by some advanced machine learning methods. The study compares various models such as; Random Forest, AdaBoost, Support Vector Machine, CatBoost, Decision Tree, LS-SVM, Logistic Regression, Naïve Bayes, k-Nearest Neighbors, LightGBM, XGBoost, and neural networks. Most emphasis is put on TriBoost that is a novel ensemble model which integrates XGBoost, CatBoost, and LightGBM. Lastly, the effectiveness of a model is determined using measures such as ROC AUC and accuracy. Evaluation of the results has shown that the proposed TriBoost had the highest accuracy at 94% and ROC AUC of 91% thereby outcompeting the use of individual classifiers as well as other ensemble methods. The results of the study confirm adequacy and possibilities of ensemble learning to enhance predictive precision and stability and their applicability for early detection of diabetes in clinics. Further work shall seek to increase the variability of the dataset, integrate fresh clinical data, and increase computational effectiveness for practical use.

关键词： Support vector machines Renewable energy sources Accuracy Sensitivity Predictive models Stability analysis Diabetes Ensemble learning Decision trees Random forests

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APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service 19

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Le...

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19th IEEE International Conference on e-science, e-science 2023

作者： Li, Zilinghan He, Shilan Chaturvedi, Pranshu Hoang, Trung-Hieu Ryu, Minseok Huerta, E.A. Kindratenko, Volodymyr Fuhrman, Jordan Giger, Maryellen Chard, Ryan Kim, Kibaek Madduri, Ravi Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85281 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States University of Chicago Consortium for Advanced Science and Engineering ChicagoIL60637 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States

ISBN: (纸本)9798350322231

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://*** © 2023 IEEE.

关键词： Privacy-preserving techniques

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LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and Novel Feature Fusion for Object Detection on Drone-Captured Scenarios

LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and ...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Wangyu Jiang Le Wang Guojun Mao Meng Sun Fayaz Ali Dharejo Ghulam Ali Mallah Fujian Provincial Key Lab of Big Data Mining&Applications Fujian University of Technology Fuzhou Fujian Province China Department of Electrical Engineering and Computer Science Khalifa Univeristy AbuDhabi UAE Department of Computer Science Shah Abdul Latif University Khairpur Pakistan

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

To address challenges in drone-captured images, including small-sized objects, multiple scales, and object diversity, we propose a novel lightweight and feature fusion based object detection model, called LF-YOLOv7. Firstly, a comprehensive high-resolution refinement design is investigated through simultaneously restructuring the head and optimizing the backbone, achieving more accurate assignment of multiple anchor boxes of different sizes and higher spatial resolution with lightweight mode. Furthermore, we present a new feature fusion module SM-BiFPN, which effectively integrates shallow details through cross-layer connections and enables the model to obtain more feature information related to small objects. Experimental evaluation shows that our method achieves a more competitive results than the benchmark and other compared state-of-the-art models in terms of mAP50 and the number of parameters.

关键词： Cross layer design Head Accuracy Scientific computing Computational modeling Object detection Benchmark testing

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Deep Adaptively Feature Extracting Network for Cervical Squamous Lesion Cell Detection 4th

Deep Adaptively Feature Extracting Network for Cervical Squ...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Peng, Zhonghua Hu, Rong Wang, Fuen Fan, Haoyi Eng, Yee Wei Li, Zuoyong Zhou, Liwei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fujian Fuzhou350118 China School of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fujian Fuzhou350121 China Department of Nutrition The First Affiliated Hospital of Zhengzhou University Henan Zhengzhou450052 China

ISBN: (纸本)9783031200984

Cervical cancer is one of the most widespread malignancies affecting women’s health worldwide today. However, the task of detection is particularly difficult due to the complex background of the cervical smear, where cells are previously stacked in clusters. To address this problem, we utilize the YOLOv5 as the baseline and build on YOLOv5 by using the simple Transformer Block which only combines with multi-head self attention layers and MLP to better extract cell features as well as to obtain global information. In addition, we allow the model to refine features adaptively to assist with detection by using Convolutional Block Attention Module (CBAM), an attention module being simple and effective for feed-forward convolutional neural networks, in the complex background information. Finally, we compare the model with YOLOv5 as baseline. In CDetector dataset, our model obtains 52.5% mAP@.5, which is 6% better than baseline. In transfer learning, it is 62.2%, which outperforms baseline by 3.2%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cells

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Development of Advanced Noise Filtering Techniques for Medical Image Enhancement

Development of Advanced Noise Filtering Techniques for Medic...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Sumit Kushwaha K. Amuthachenthiru Geetha. K Jonnadula Narasimharao Dileep Kumar M Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Telangana India

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality.

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