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Event- Triggered based Control of Robotic Arm Under Denial of Service

Event- Triggered based Control of Robotic Arm Under Denial o...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Abdulrazaq Nafiu Abubakar Jamilu Umar Yahaya Muhammad Ma'aruf Muhammad Faizan Mysorewala Abdul-Wahid A. Saif Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum and _ Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

In this paper, we studied the finite-time stability of robotic arm systems against Denial of Service attacks, when the link between the plant and the controller is often interrupted. In order to do this, a method is suggested that relies on an event-triggered mechanism which is based on the rate of change of the Lyapunov function, that keeps the system finite-time stable while simultaneously reducing the quantity of data interchange in the network. The suggested event-triggered finite-time controller's vulnerability to Denial of Service attacks is then assessed, and sufficient limitations on the length of a Denial of Service are identified to ensure the system's stability within a specified time frame. Lastly, a simulation study is used to show how effective the suggested strategy is.

关键词： Time-frequency analysis Service robots Manipulators Sampling methods stability analysis Logic design Lyapunov methods

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Machine learning-Based Thickness Estimation of Abdominal Fat and Muscle Using Simulated Radio Frequency Scattering Parameters

Machine Learning-Based Thickness Estimation of Abdominal Fat...

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international conference on Electrical engineering, Computing Science and Automatic Control (CCE)

作者： Alfredo Bayu Satriya Myles Joshua Toledo Tan Yong-Kyu Yoon Electrical & Computer Engineering University of Florida Gainesville FL United States

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

The estimation of three abdominal tissue thicknesses, namely subcutaneous fat (SAT), muscle tissue (MT), and visceral fat (VT), was performed using radio frequency (RF) signal measurement and machine learning algorithms. The scattering parameter data were collected from simulations of a planar multilayered model of the human abdomen, with parametric analysis on each layer's thickness. Various models, incorporating combinations of several frequency bands and 120 data points, were investigated. The results showed that polynomial regression (PR) and ridge polynomial regression (RiPR) using scattering parameters from frequency bands of 300 MHz, 400 MHz, 500 MHz, and 1 GHz performed the best among other models. PR (300 MHz, 400 MHz, 500 MHz, and 1 GHz) achieved RMSE values of 0.71 mm for SAT estimation, 1.2 mm for MT estimation, and 1.17 mm for VT estimation. RiPR (300 MHz, 400 MHz, 500 MHz, and 1 GHz) achieved RMSE values of 0.35 mm for SAT estimation, 1.57 mm for MT estimation, and 1.7 mm for VT estimation. However, these results indicated a high error percentage for VT estimation, as VT had a mean thickness of only 3 mm. The results also showed that the deeper the tissue, the more prone the estimation was to error. This is due to the nature of electromagnetic reflection and transmission within a multilayered medium, which affects the scattering parameter measurement. While the models perform well for upper tissue estimation, further improvements in the model, feature selection, and physical solutions are necessary to enhance the accuracy of deeper tissue estimation.

关键词： Estimation Muscles Polynomials data models Frequency estimation Scattering parameters Fats Impedance Tuning Abdomen

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Democratizing the Federation in Federated learning

Democratizing the Federation in Federated Learning

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IEEE Internatonal conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Ningxin Su Baochun Li Bo Li Department of Electrical and Computer Engineering University of Toronto Department of Computer Science Hong Kong University of Science and Technology

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Federated learning (FL) is a widely acknowledged distributed training paradigm that preserves the privacy of data on participating clients, and has become the de facto standard for distributed machine learning across a large number of edge devices. Conventional FL, however, has a rather rigid design, where the server is the dominant player that selects a subset of its clients to participate in each communication round, and clients are merely followers, and are not offered the freedom to accept or decline invitations from the server to participate. In addition, clients may become unavailable or very slow due to a wide variety of reasons, yet it may take an excessive amount of time for a conventional FL server to recognize that a particular client is unavailable. In this paper, we advocate for a more pragmatic paradigm in federated learning, called democratic federated learning, to offer more freedom to both servers and clients with respect to the ability to accept or decline requests, and to explicitly request to participate. In contrast to conventional federated learning, our paradigm allows (1) both the server and clients to participate and withdraw from the federated learning process at any time; (2) the server to decide whether to reject clients' updates based on the current model convergence steps, i.e., after satisfying the minimum required clients' updates received; and (3) the clients to adjust the local epochs based on their own training and communication time. Our experimental results on a variety of datasets and models have confirmed that democratic federated learning not only accelerates the convergence process but also improves the accuracy of converged models, and serves as a foundation for future explorations into client-centric models within the FL ecosystem.

关键词： Training data privacy Federated learning Ecosystems Distributed databases Smart systems Servers standards Convergence Pragmatics

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data-Driven with Process Mining to Analyze and Identify Key Features for No-Show Risks in Patients with Chronic Diseases to Achieve Treatment Improvement

Data-Driven with Process Mining to Analyze and Identify Key ...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Kantapat Chaichareenon Natawut Nupairoj Department of Computer Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study addresses the significant challenge of chronic diseases, such as diabetes, which require continuous medical care and follow-up. It aims to visualize the revisit interval that affects patient treatment with multi-level process mining and determine factors that cause patients to miss their appointments or “no-show” by using machine learning. Our research demonstrates that patients who have improvement have significantly shorter revisit intervals compared top atients who are deteriorating in their treatment, with a P-value of 3.83x10 −9 . A primary challenge is the substantial number of patients failing to attend their appointments. To uncover the reasons behind these no-shows, we employ PyCaret which facilitates the comparison of the performances of 14 models. This approach enables us to determine the importance of various factors contributing to patient no-shows, including our features from multi-level process mining. The result was that patients who usually miss their follow-up schedules have a higher chance of “no-show” at their next appointment. A significant root cause i s “historical attendance of individual patients” which is one of the features from multi-level process mining analysis. Our model has an AUC score of 0.80 in predicting the no-show risk of diabetes patients by using the Gradient Boosting Classifier. O ur study shows that exploring important features can be discovered by the exploration data analysis process. This insight offers a pathway to enhance patient engagement and reduce missed appointments, ultimately aiming to improve care for individuals with diabetes.

关键词： Process mining Training Analytical models Costs Medical services Predictive models data models

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Exploring the Impact of Error Type Features Integration on Transformer-based Myanmar Spelling Correction

Exploring the Impact of Error Type Features Integration on T...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Ei Thandar Phyu Ye Kyaw Thu Hutchatai Chanlekha Kotaro Funakoshi Thepchai Supnithi Faculty of Engineering Kasetsart University Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Spelling correction is a fundamental aspect of natural language processing systems, essential for ensuring the accuracy and fluency of text-based applications. In the context of neural machine translation based spelling correction, spelling errors pose significant challenges, particularly in low-resource language like Myanmar language where training data is limited. In this paper, the Myanmar spelling error-correction parallel corpus was developed through the collection of text from social media platforms. By learning the error patterns in social media text data, we categorized the Myanmar spelling mistakes into ten categories. We developed Myanmar spelling correction model using Transformer architecture and also investigated the potential improvement of our Transformer-based spelling correction by incorporating error type features as additional linguistic information. Evaluating the Transformer model with manually annotated error type labels in our developed corpus significantly enhances the F1-score from 75.91% to 86.71%, increases the GLEU score from 0.9648 to 0.9767, and decreases the word error rate from 3.3% to 2.0%. Additionally, we conducted a preliminary study on integrating our proposed correction model with an automatic error type classification model, employing a bidirectional Long Short-Term Memory. The findings demonstrate that integrating error type information into the Transformer architecture enhances the effectiveness of Myanmar spelling correction.

关键词： Social networking (online) Computational modeling Training data Neural machine translation Computer architecture Linguistics Transformers

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Design and Implementation of Power Efficient Hardware Accelerator for Bidirectional Long Short- Term Memory (Bi-LstM)

Design and Implementation of Power Efficient Hardware Accele...

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IEEE India conference (INDICON)

作者： K. N. Apoorva Vivek Venugopal Chinthala Ramesh Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

In recent years, deep-learning models have become increasingly complex, leading to a surge in demand for specialized hardware accelerators. Long Short-Term Memory also known as LstM, is an extensively used neural network for sequential data processing. This paper presents the design and implementation of a hardware accelerator for bi-directional LstM (Bi-LstM), op-timized for power consumption and performance improvements with respect to state-of-the-art FPGA-based accelerators. The accelerator uses column bypass multiplier based column-wise matrix-vector multiplication to avoid unnecessary computations on zero-valued input data and reduce the data dependency which enables higher parallelism leading to reduced overall compute time. The Bi-LstM design is simulated to verify its functionality and synthesize it on a target Xilinx Zync-7000 FPGA. The results demonstrate that the proposed accelerator achieves lesser power consumption with an increased frequency by 23.45 % and a 5.45x increase in energy efficiency when compared to state-of-the-art hardware accelerators for Bidirectional LstM.

关键词： Power demand Neural networks Bidirectional long short term memory Parallel processing Energy efficiency Software Real-time systems Surges Hardware acceleration Field programmable gate arrays

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Design and Development of Scalable SIEM as a Service using Spark and Anomaly Detection

Design and Development of Scalable SIEM as a Service using S...

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international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Thean Thepa Pongsapon Ateetanan Pratuck Khubpatiwitthayakul Somchart Fugkeaw School of ICT Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Existing Security Incident and Event Management (SIEM) systems traditionally gather data from various log files produced by applications and network security systems like firewalls and IDS/IPS. Modern SIEMs now integrate machine learning capabilities to analyze log file patterns, learning from them to detect and potentially prevent similar attacks while adapting to evolving threats. However, deploying AI-driven SIEM solutions on the cloud to serve multiple clients still presents challenges due to the diverse and voluminous nature of log files, leading to significant preprocessing costs for managing various file formats and unpredictable throughput. In this paper, we proposed a dynamic and efficient SIEM as a service model called SIEMAS integrating Random Forest (RF) as the core detection engine. Additionally, we introduced log file normalization based on Amazon Kinesis and utilized Apache Spark to streamline this normalization process. Our detection approach uses anomaly detection to instantly recognize patterns of malicious events and respond to them by leveraging the RF algorithm. Finally, we have developed a prototype as a proof of concept of the system functionality and conducted experiments to demonstrate its detection accuracy and performance, which have shown to be efficient in practical scenarios. The results showed that our proposed solution achieves an overall detection accuracy of 98% while achieving a 33 % higher speed of detection than without using distributed computing.

关键词： Radio frequency Accuracy Prototypes Cluster computing Network security Feature extraction Sparks

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A Comparative study of Adaptive Time Series Models for Weather Forecasting in Indian Scenarios

A Comparative Study of Adaptive Time Series Models for Weath...

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IEEE India conference (INDICON)

作者： Shalini Sharma Upendra Pratap Singh Upgrad Education Pvt. Ltd Mumbai India Liverpool John Moores University Liverpool UK Department of Computer Science and Engineering The LNM Institute of Information Technology Jaipur Rajasthan

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Given the erratic changes in the weather and climate patterns across the globe and their ramifications on the livelihood, safety and security of human and animal communities, weather forecasting has evolved as a pressing task to ensure the sustainability and security of these communities. In this work, the temperature data of different places across India are considered for training different conventional and deep learning-based time series models; specifically, while ARIMA and SARIMAX models are trained in conventional settings, LstM networks are trained in deep learning settings. The performance of these models is evaluated using AIC, BIC and HQIC metrics, followed by a systematic comparison of their modelling performances. Empirical results show that LstM-based models capture long-term dependencies in the time series data better than conventional time series models; this improved modelling using LstMs is quantified using AIC, BIC and HQIC scores for different places across India. The AIC, BIC, and HQIC values reported by the LstM models are several orders of magnitude lesser than those reported by ARIMA and SARIMAX models; consequently, LstMs are then deployed for time series prediction using temperature data of places considered in the study.

关键词： Adaptation models Temperature distribution Recurrent neural networks Time series analysis Weather forecasting Predictive models data models Security Long short term memory Context modeling

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Customer Churn Prediction Analysis for Retention Using Ensemble learning

Customer Churn Prediction Analysis for Retention Using Ensem...

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international conference on Computing and Networking Technology (ICCNT)

作者： J. Charanya T. Sureshkumar V. Kavitha I. Nivetha S. Dhamu Pradeep C. Ajay Department of Artificial Intelligence Kongu Engineering College Perundurai Erode Tamil Nadu India Department of Information Technology Nehru Institute of Technology(Autonomous) Coimbatore Tamil Nadu India Department of Computer Science and Engineering Velalar College of Engineering & Technology Erode Tamil Nadu India Department of Electronics and Communication Engineering Kongu Engineering College - Perundurai Erode Tamil Nadu India Department of Electronics and Communication Engineering Kongu Engineering College-Perundurai Erode Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the 21st century, there is a handful of information on the Internet. It is easy for customers to switch to a competing company in any sector. It is important for a company to retain its existing customers rather than secure new ones. So, customer retention becomes an essential part of an organisation’s business. Customer churn prediction is a type of data analysis based on the historical data of any organization. It helps to predict whether the customers are going to leave the organisation or not in advance. Also, it helps the organisation predict what customers are expecting from them and where they are lacking. There are a wide range of algorithms used for churn prediction. However, there is no agreement on choosing the best one. Therefore, this project presents customer churn prediction using the ensemble method in the telecommunications sector. The ensemble approach amalgamates predictions from base classifiers such as decision trees, random forests, gradient-boosting machines, and support vector machines. By studying the strengths of these models, ensemble techniques like bagging, boosting, and stacking mitigate individual model biases and variance, leading to improved predictive performance. We are using the XGBoost and Random Forest methods to predict the churn. It is a type of ensemble method. The ensemble method is used to increase accuracy by combining multiple models rather than using a single model. From the result, we have observed that the proposed model, XGBoost and Random Forest, attains 99% accuracy. Overall, the project adds significant value by enabling proactive churn management, customer-centric strategies, and sustainable business growth.

关键词： Analytical models Adaptation models Accuracy Companies Predictive models data models Telecommunications Ensemble learning Random forests Tuning

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stC: student-Teacher Collaborative Model for Multi-Target Domain Adaptation

STC: Student-Teacher Collaborative Model for Multi-Target Do...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Xu Jiayi Yu Jiaqi Wang Yixin Han Shijia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair.

关键词： Bridges Adaptation models Federated learning Large language models Estimation Object detection Information processing Media Explosions Periodic structures

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