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RIS-Aided Federated Edge Learning Exploiting Statistical CSI

RIS-Aided Federated Edge Learning Exploiting Statistical CSI

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Li, Heju Wang, Rui Wu, Jun Zhang, Wei Soto, Ismael Tongji University Dept. Electronics and Information Shanghai200092 China Fudan University Dept. Computer Science Shanghai200433 China The University of New South Wales Dept. Electrical Engineering and Telecommunications SydneyNSW2052 Australia University of Santiago Dept. Electrical Engineering AE Santiago de Chile3519 Chile

ISBN: (纸本)9798350345384

Federated edge learning (FEEL) as an emerging distributed learning paradigm can effectively resolve the resource constraints and privacy issues in the Internet of Things (IoT) by featuring collaborative training at the edge devices under the premise of data localization. Nevertheless, owing to the scarce resources and the inscrutable communication fading, issues such as model damage and signal deviation will critically diminish the convergence performance and learning accuracy of FEEL. With this in mind, reconfigurable intelligent surface (RIS) has recently been integrated to enhance the communication quality of wireless systems by adaptively reconfiguring the signal propagation environment. To utterly release the applied potency of RIS, the configuration of phase shifts is of paramount importance, where accurate channel state information (CSI) is required. Unfortunately, the accurate instantaneous CSI is extremely challenging to be estimated. In this paper, we focus on the realistic scenario assuming only statistical CSI is known, which can be comparatively easily and accurately explored. On the other hand, considering the wireless outage caused by the random non-line-of-sight channel, we rigorously derive an explicit convergence upper bound of the RIS enabled FEEL framework with respect to the outage probability. Based on the convergence theory, a unified resource allocation problem is further established by jointly optimizing bandwidth allocation, and the RIS configuration matrix. Extensive simulations are conducted to demonstrate that the proposed design dramatically promotes the learning performance compared against the baseline solutions. © 2023 IEEE.

关键词： Channel state information

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Rasio Fernandis Daniel Swanjaya Risky Aswi Ramadhani Patmi Kasih Julian Sahertian Dept. Informatics Engineering Faculty of Engineering and Computer Science Universitas Nusantara PGRI Kediri Kediri

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children’s development at school through the results of each semester’s assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children’s anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children’s anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children’s anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children’s anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes.

关键词： Analytical models Data preprocessing Education Coherence Libraries Data models Resource management Information technology

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A Multi-Scale Learning Assisted Neural Network for Improved Object Detection in Complex Environments 9

A Multi-Scale Learning Assisted Neural Network for Improved ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Karthikayani, K. Vadlamani, Subramanyam M. Prasad, M.V.Siva Thatipudi, Jayendra Gopal Kalaivaani, P.C.D. Upadhyay, Shrikant Srm Institute of Science and Technology Chennai India Anurag engineering college Department of Cse Kodad India University of North Texas Dept. of Computer Science United States Kongu Engineering College Dept of Cse Perundurai638060 India Mlr Institute of Technology Department of Ece Hyderabad India

ISBN: (纸本)9798350377972

Object detection in complex environments presents significant challenges due to the variability in object scales, occlusions, and cluttered backgrounds. This paper proposes a Multi-Scale Learning Assisted Neural Network designed to improve object detection accuracy in such scenarios. The proposed model integrates multi-scale learning, hybrid attention mechanisms, and an advanced feature fusion strategy to enhance its ability to detect objects across various scales and complexities. The model was evaluated against nine state-of-the-art object detection models, including YOLOv4, Faster R-CNN, SSD, EfficientNet, RetinaNet, CenterNet, Cascade R-CNN, FCOS, and Mask R-CNN. Experimental results demonstrate that the proposed model outperforms existing models, achieving an accuracy of94.25%, precision of93.50%, recall of92.70%, and an F1-score of93.10%. Additionally, the model exhibits a balanced trade-off between inference time and model size, making it suitable for real-world applications where performance and resource efficiency are critical. The high IoU (87.50%) and mAP (89.20%) scores further validate the model's robustness in accurately detecting and localizing objects in complex environments. This study highlights the potential of the proposed model to significantly improve object detection tasks, particularly in challenging and dynamic real-world scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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Identification of Fraudulent Transactions for Enhanced Credit Card Fraud Detection 4

Identification of Fraudulent Transactions for Enhanced Credi...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Monica Gayathri, V. Narula, Rachna Kumar, Vijay Kaur, Surinder Singh, Neetu Bharati Vidyapeeth's College of Engineering Dept. of Electronics and Communication Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India Bharati Vidyapeeth's College of Engineering Department of Information and Technology New Delhi India

ISBN: (纸本)9798350387490

With the flourishment of the digital era and the evolution of new techniques and technology, this paper highlights the ever-growing risk of credit card fraud. The advancing technology has provided fraudsters with new and untraceable techniques for scamming genuine users. With the increasing impact of fraud, financial institutions must use machine learning tools for scam detection to provide safety and security to their users' accounts. This paper consists of an interdisciplinary comparative analysis of ML algorithms: Random Forest Classifier, XGBoost, ADABoost, Logistic Regression, Decision Trees, and Artificial Neural Networks. The diverse selection of algorithms aims to determine the most effective algorithm with the highest accuracy score for predicting legitimate and fraudulent transactions. The methodology involves fetching data from an Excel file, training the models, testing them on 20% of the dataset, and finally obtaining results in a Word document, having a confusion matrix and accuracy of each algorithm. By comparing the results of the confusion matrix and accuracy score, the Random Forest Classifier and XGBoost demonstrate the highest accuracy scores, while Logistic Regression yields a lower accuracy score. © 2024 IEEE.

关键词： Logistic regression

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A Hybrid YOLO-Based Approach for Fine-Grained Detection of Classroom Student Behaviors

A Hybrid YOLO-Based Approach for Fine-Grained Detection of C...

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International Conference on computer and Information Technology (ICCIT)

作者： Hridoy Das Hazera Khatun Hira Mahtab Uddin Apu Kumar Roy Asif Mahmud Dept. of Computer Science and Engineering United International University Dhaka Bangladesh Institute of Natural Sciences United International University Dhaka Bangladesh Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper introduces a system for real-time identification of student behavior in classrooms, employing YOLOv8 for precise object detection and Convolutional Neural Networks (CNNs) for behavior analysis, this technology enables the identification of body language to categorize behaviors such as attentiveness and participation. The scalable solution is guar-anteed to work effectively for various classroom sizes due to the implemented approach. The system possesses the potential to revolutionize classroom management, as evidenced by its initial findings demonstrating its accuracy. Furthermore, this technology facilitates the provision of immediate feedback to teachers, while simultaneously enabling them to identify long-term behavioral patterns. It serves as a valuable tool that assists teachers in delivering tailored instruction and fostering enhanced student engagement. The source code and models are publicly released: GitHub Repository.

关键词： YOLO Accuracy Source coding Writing Real-time systems Convolutional neural networks Object recognition Monitoring Software development management Residual neural networks

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High-Performance Face Identification using Hyperspectral Imaging to Counteract Deep Fake Biometrics

High-Performance Face Identification using Hyperspectral Ima...

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IEEE International Conference on Robotic Computing (IRC)

作者： Sota Furusawa Chinthaka Premachandra Dept. of Electrical Engineering and Computer Science Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

This research addresses the security challenges posed by Generative Adversarial Networks (GANs) in biometric authentication, focusing particularly on the use of Hyperspectral Imaging (HSI) to counteract the threat of deep fake biometrics created by GANs. We sought solutions to the issues identified in a previous study, such as the inability to handle images of individuals not previously trained and the high time consumption required for identification. In this paper, we first reduced the dimensionality of hyperspectral face data used for training, and then conducted a binary classification of “target subject (class)” and “nontarget subjects (classes).” A discriminator, based on the Least Squares Generative Adversarial Network (LSGAN) model, was developed using HSI for each class for binary classification. The proposed method significantly reduced identification time and achieved very high accuracy in identification.

关键词： Training Deepfakes Face recognition Biological system modeling Focusing Generative adversarial networks Security Robots Hyperspectral imaging Principal component analysis

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Genetic Effects of Covid 19 on the Development of Neurodegenerative Diseases 5

Genetic Effects of Covid 19 on the Development of Neurodegen...

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5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Podder, Nitun Kumar Shill, Pintu Chandra Rana, Humayan Kabir Omit, Shudeb Babu Sen Mehedee Hasan Al Shahriar, M. Shafiul Azam, M. Khulna University of Engineering Technology Dept. of Computer Science Engineering Khulna Bangladesh Green University of Bangladesh Dept. of Computer Science Engineering Dhaka Bangladesh Z. H. Sikder University of Science and Technology Dept. of Computer Science Engineering Shariatpur Bangladesh Pabna University of Science and Technology Dept. of Computer Science Engineering Pabna Bangladesh

ISBN: (纸本)9781665409063

The coronavirus disease (COVID-19) caused by SARS-COV-2, a highly infectious pathogen, genetically similar to SARS-COV is an unprecedented worldwide health crisis. Rapidly accumulating clinical research revealed that COVID-19 is manifested by various neurological symptoms and also affects the brain in many ways including direct infection to systemic inflammation. Which indicates it may substantially increase the incidence of developing neurodegenerative diseases (NDGDs). To discourse this issue we studied the computable frameworks to address the gene expression association of COVID-19 and NDGDs to identify the link among them. We analyzed GEO microarray datasets from COVID-19 and NDGDs including Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways, protein-protein interaction (PPI) network, and protein-drug interaction (PDI) network. We observed that COVID-19 associated genes share 19, 26, 20, 19, 22 differentially expressed genes with Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease respectively. Gene expression dysregulation, PPI and PDI relationship networks, different pathways suggest that COVID-19 may have a significant link to the development of these NDGDs. This analysis may help to develop therapeutic strategies and raise awareness about the influence of COVID-19 on the progression of NDGDs. © 2021 IEEE.

关键词： Proteins

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On the Pressure Sensing of Biological Fluids Using Microwaves

On the Pressure Sensing of Biological Fluids Using Microwave...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Gaddam, Sunil Samaddar, Poulami Gopalakrishnan, Keerthy Damani, Devanshi Shivaram, Suganti Dey, Shuvashis Mitra, Dipankar Roy, Sayan Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine RochesterMN United States Mayo Clinic Department of Cardiology RochesterMN United States Mayo Clinic Department of Pharmacology RochesterMN United States North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States South Dakota Mines Dept. of Electrical Engineering and Computer Science Rapid CitySD United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (纸本)9781665442282

In vivo biological fluid pressures have been linked to many diseases. Elevated blood pressure is a major risk factor for heart disease that affects millions of people across the world. Non-invasive blood pressure monitoring techniques have been limited to extremities of the body, like limbs, and are unable to measure dynamic internal pressures observed in deeper internal anatomical spaces like cardiac chambers, principal veins, cranial cavity, etc. Novel methods to investigate the internal biological pressures non-invasively can unlock new therapies to treat a wide range of diseases. Microwave-based sensing for biomedical applications is an active research area with the potential to expand in sensing vital physiological indicators like pressure. This study is an experimental investigation into the role of pressure on the dielectric properties of water to broaden the biomedical applications of microwave-based sensing. An experimental setup consisting of a pressure chamber with water and a co-axial probe was built to measure the dielectric properties at pressures ranging from 0 to 240 mm of Hg over a frequency range of 0.5-20 GHz. A comparison of the measurements shows a small and significant change in dielectric properties with varying pressures revealing the challenges and a direction for future scientific research. © 2023 IEEE.

关键词： Blood pressure

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Attendance Management with Facial Recognition using OpenCV

Attendance Management with Facial Recognition using OpenCV

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Emerging Technology (INCET), International Conference for

作者： Sudhakar Atchala Shaik Riyaz M. Chola Chaitanya Chippada Uday dept. Computer Science and Engineering Lakireddy Bali Reddy College of Engineering (Autonomous) Mylavaram India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In this research, we propose a comprehensive system integrating facial recognition technology and attendance management for educational institutions or organizations. Leveraging state-of-the-art face recognition algorithms and machine learning techniques, our system aims to automate the attendance tracking process, enhancing accuracy and efficiency. We conducted extensive experiments, employing a diverse dataset for training and testing. The results demonstrate the robustness and reliability of our facial recognition model in real-world scenarios. Furthermore, we implemented a user-friendly web interface for administrators and instructors to manage attendance records seamlessly. The system incorporates an automated email notification feature, sending alerts to students or relevant personnel upon successful face detection. Our research contributes to the evolving field of automated attendance systems, providing an innovative solution that not only simplifies the tracking process but also ensures data accuracy and accessibility. The implementation and evaluation of our system indicate its potential to significantly streamline attendance management practices in educational institutions and beyond.

关键词： Training Accuracy Systematics Face recognition Organizations Feature extraction Face detection

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Graph Based Keywords Extraction from Crime Data

Graph Based Keywords Extraction from Crime Data

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Priyanka Das Anuvab Munshi Annwesha Naha Adrija Dey Sriparno Chakraborty Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crime-related keywords from in-dept. police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied; following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime.

关键词： Law enforcement Prevention and mitigation Clustering algorithms Information retrieval Natural language processing Partitioning algorithms Classification algorithms Data mining Communication networks Computational intelligence

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