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Functional Suitability and Evaluation Metrics for Autonomous data Center Management: A Case Study of DCAMP

Functional Suitability and Evaluation Metrics for Autonomous...

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IEEE international conference on Software Architecture Workshops (ICSAW)

作者： Violet Deepa Sera Sumit Kalra Department of Computer Science and Engineering Indian Institute of Technology IIT Jodhpur Jodhpur India

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

In response to the escalating demand for data usage and the imperative for stringent environmental sustainability, future data centers are being conceptualized for unconventional locations such as underwater, outer space, and remote areas. These boundaryless data centers necessitate sophisticated management solutions capable of autonomous operation with minimal human intervention. The novel data Center Autonomous Management Platform (DCAMP) framework, leverages artificial intelligence, machine learning, automation, and contemporary software methodologies to construct an autonomous management platform with a focus on continuous improvement through robust knowledge management and human-in-the-loop based governance. This paper presents a comprehensive architectural evaluation of the DCAMP framework, assessing its quality attributes in accordance with ISO/IEC standards. Key performance indicators and metrics for managing boundaryless data centers are defined, and a usecase simulation evaluating the Functional Suitability characteristic of the DCAMP framework is discussed. Our findings demonstrate that DCAMP provides a compelling solution, offering high output accuracy and enhanced task efficiency, even amidst evolving requirements. The target audience for this paper includes researchers and practitioners in software and systems engineering. It is particularly relevant for data center managers, system administrators, infrastructure managers, SRE cloud engineers, and infrastructure researchers, offering them metrics and evaluation criteria to assess the effectiveness of autonomous data centers which need continuous adaptation.

关键词： data centers Automation Uncertainty Software architecture Machine learning Systems engineering and theory Human in the loop Software Standards Next generation networking

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Lung Cancer Detection using Machine learning Approach

Lung Cancer Detection using Machine Learning Approach

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Md Abrar Hamim F. M. Tanmoy Umme Fatema Tuj Asha Md Nazmul Haq Maruf Alam Bijoy Ghosh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering University of South Wales Wales United Kingdom

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The detection of lung cancer is a critical factor in enhancing patient survival rates. The integration of intelligent computer-aided systems can significantly aid radiologists in this endeavour. The present study centres on the development of a machine learning-oriented methodology aimed at detecting lung cancer through the analysis of text-based medical data extracted from authentic medical reports. The present dataset encompasses a range of machine learning algorithms that have been utilised for binary classification purposes. These algorithms include Support Vector Machines (SVM), Logistic Regression, Gradient Boosting, and Catboost. The objective of this study is to determine the optimal algorithm for the detection of lung cancer with the highest degree of accuracy. By means of comprehensive experimentation, the SVM and Logistic Regression models yielded the highest accuracy rates of 95% and 94%, respectively. The findings of this study indicate the capability of machine learning algorithms in the prompt identification of lung cancer, thereby facilitating enhanced diagnosis and timely intervention.

关键词： Support vector machines Logistic regression Privacy Machine learning algorithms Lung cancer Predictive models Robustness

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TWAFR-GRU: An Integrated Model for Real-time Charging Station Occupancy Prediction 19

TWAFR-GRU: An Integrated Model for Real-time Charging Statio...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Qiyang Liu, Sheng Qu, Haohao Zhu, Rui You, Linlin Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Systems Science Department Singapore

ISBN: (纸本)9798350346558

The fast-growing number of electric vehicles has resulted in a variety of charging-related issues, such as the shortage of public charging piles, the inconvenience of finding available charging piles, the additional traffic congestion caused by unnecessary cruising to search for available charging piles, etc. Since only constructing new charging piles may not significantly improve the charging efficiency, methods of predicting charging station occupancy are widely discussed as an effective means to address these issues. However, several challenges are encountered in training an efficient and effective model by utilizing distributed data, improving convergence speed, and enhancing model generalization ability. To address these issues, this paper proposes a novel mechanism, named TWAFR-GRU, which integrates Temporally Weighted Asynchronous Federated learning (TWAFL) with Reptile and Gated Recurrent Unit (GRU). As shown by the holistic evaluation based on the charging station occupancy dataset, compared with other state-of-the-art baselines, TWAFR-GRU can 1) decrease MAE, RMSE and RAE by 19%, 15% and 17% separately and improve R2, by 67%;2) cut rounds for the model to converge by 75%;3) save training time by 44%;and 4) reduce 17% in forecasting error after the personalization of the initial model to serve a specific charging station. © 2022 IEEE.

关键词： Piles

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Designing assessment processes using the student involvement method by WTCAi system

Designing assessment processes using the student involvement...

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international Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Éva Karl Enikő Nagy György Molnár Várkerti Primary School Várpalota Hungary Doctoral School of Multidisciplinary Engineering Sciences (MMTDI) Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty Institute of Cyberphysical Systems Óbuda University Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary

The quality assurance of education and training is of paramount importance, and the monitoring, assessment and evaluation process is indispensable for this purpose. A key determinant of the effectiveness of education is assessment, which serves not only to measure performance but also to support the learning process. The process of assessment and evaluation must take into account the diverse needs and abilities of students. Differentiated assessment supports individual learning paths and personal development. Assessment must be objective and valid, but it can achieve its deeper developmental purpose when personalized. Our research aims to enhance pedagogical assessment efficiency by developing a machine learning-supported assessment system. The research focuses on developing STEM skills and analyzes children's questions to understand their interests and needs better. During the research, we observe the characteristics of questions posed by children and develop a machine learning algorithm that helps provide suggestions to educators for creating assessment questions. Based on the data, we identify children's areas of interest and the difficulty level of questions. According to the research results, children's questions can significantly differ from question sets prepared by educators, suggesting that children's diverse perspectives and interests should be given even greater consideration in teaching. The research also raises further directions for development. It aims to use machine learning to understand better the diverse ways of thinking and differences in expression among children, thereby making education and assessment more effective. This can contribute significantly to the efficiency and effectiveness of the teaching-learning process.

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Complex Road Recognition CNN Network Based on Multi-Label learning

Complex Road Recognition CNN Network Based on Multi-Label Le...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Haoxiang Gan Han Zhang Wanzhong Zhao Yuhan Liu Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The road information preceding the vehicle serves as a fundamental prerequisite for intelligent driving systems. In comparison to other data requirements of autonomous vehicles, road information exhibits a more two-dimensional character. Among the array of sensors currently equipped on autonomous vehicles, it is only the visual sensors that possess the capability to detect and interpret the road surface. The majority of road recognition algorithms rely heavily on multi-classification methods, which, however, tend to introduce significant data imbalance issues. The process of further refining pavement types exacerbates this problem. Based on the Road Surface Classification dataset (RSCD), this study uses the diffusion model to generate category images with less data, and uses the traditional image augmentation method to augment the original image dataset. Furthermore, a convolutional neural network-based multi-label classification model, incorporating the Zero-bounded Log-sum-exp& Pairwise Rank-based loss functions, is trained to enhance the model's predictive performance and accuracy. This enhanced model ultimately achieved an average accuracy of 86.8%, demonstrating significant improvements in performance.

关键词： Visualization Image recognition Accuracy Roads Predictive models Convolutional neural networks Surface treatment intelligent sensors Autonomous vehicles Sensor arrays

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Network based Intrusion Detection using Time aware LSTM Autoencoder

Network based Intrusion Detection using Time aware LSTM Auto...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Ritesh Ratti Sanasam Ranbir Singh Sukumar Nandi Department of Computer Science & Engineering Indian Institute of Technology Guwahati Assam India

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

With the advancement of Internet technologies Cyber attacks have become a significant risk to overall security, therefore, intelligent security systems are required to strengthen the network security against these threats. Machine learning has played a pivotal role in the detection and mitigation of these attacks over the years. However, to identify the zero-day attacks and incorporate frequently changing attack scenarios, techniques need to be developed that can work with minimally labeled data. In this paper, we propose Time aware LSTM Autoencoder-based learning approach to detect the attack in network flows by training the model using only normal traffic and using reconstruction error as the parameter to classify the attack event. We perform the experiments on different recent datasets like CICDDoS2019, & CICIDS2018 and experimental results exhibit that the proposed model overall provides better classification metrics.

关键词： Training Measurement Privacy Intrusion detection Machine learning Computer architecture Network security

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Non-relational multi-level caching for mitigation of staleness & stragglers in distributed deep learning 21

Non-relational multi-level caching for mitigation of stalene...

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22nd international Middleware conference, Middleware 2021

作者： Ravikumar, Aswathy Vit Chennai India

ISBN: (纸本)9781450391559

For efficient distributed deep neural network design, mitigation of stale gradients and stragglers is necessary. The stale gradient problem occurs during the distribution and parallelism of the deep neural networks on the multi-cluster/nodes. The proposed solution for stragglers is to use distributed non-relational database to update the intermediate results of weights and their respective nodes. The results from the database are given to the parameter server. If any delay in the parameter data due to straggling is detected, immediately the straggled data will be configured in another node as a server-less function. In this approach, each node is equipped with distributed in-memory cache and a non-relational database at the parameter server. The parameter server node is an intelligent node working based on a runtime threshold set and the error analysis for fixing the optimal value of 'K' for the K-SGD. The proposed solution for stale data is to efficiently utilize the multiple GPU with multiple levels of caching in the cloud for better performance and reduced response time. Response time is reduced by offloading and pushing data close to the nodes in multiple levels of distributed cache. Using GPU cache and Elastic Cache, data is updated to the individual nodes in optimal time intervals. Thus, an integrated solution for stragglers and staleness in both data-parallel and model parallel distributed deep learning networks. © 2021 Owner/Author.

关键词： Deep neural networks

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Digital Twin for Resilient Power Distribution Systems

Digital Twin for Resilient Power Distribution Systems

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international conference on intelligent Systems Applications to Power Systems (ISAP)

作者： Baza Somda Chen-Ching Liu Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA

ISBN: (数字)9798331531751

ISBN: (纸本)9798331531768

Heightened awareness of the impact of climate change has led to the rapidly increasing penetration of renewable energy resources in electric energy distribution systems. Those distributed energy resources (DERs), mostly inverter-based, can act as resilience sources for the grid but also introduce new control, stability, and cybersecurity challenges. This work proposes a digital twin (DT) that combines a real-time electromagnetic transient power system simulation and a practical model for communication network simulation. The digital twin allows the testing of novel control and cybersecurity strategies, including machine learning-based anomaly detection. Simulations using the Virginia Tech Electric Service (VTES) as a test case demonstrate the capability of adequately controlled resources, including solar PV, energy storage, and a synchronous generator, to enhance resilience by providing energy to critical loads. The DERs comply with IEEE disturbance ride-through requirements, and switching transients are maintained within acceptable limits. A comprehensive DER-based resilience plan is developed and validated for the Virginia Tech Smart Grid.

关键词： Switches Power system stability Synchronous generators Stability analysis Digital twins Smart grids Communication networks Transient analysis Resilience Testing

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Keratoconus Severity Staging Using Random Forest and Gradient Boosting Ensemble Techniques

Keratoconus Severity Staging Using Random Forest and Gradien...

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

作者： Zahra J. Muhsin Rami Qahwaji Ibrahim Ghafir Muawyah Al Bdour Saif AlRyalat Mo'ath AlShawabkeh Majid Al-Taee Faculty of Eng. and Digital Technologies University of Bradford UK Scool of Medicine The University of Jordan Amman Jordan Department of Ophthalmology The Hashemite University Zarqa Jordan Independent Consultant of Computing and Systems Engineering Liverpool UK

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Accurate keratoconus (KC) staging is vital for improving patient care and guiding clinical decision making. Choosing the right Machine learning (ML) algorithm is key to effectively tackling this challenge and ensuring optimal performance. This paper presents a detailed comparison of eight ML algorithms commonly used in KC detection, based on a clinical dataset collected by the authors over the past decade. The study investigates each algorithm's effectiveness in distinguishing KC severity stages. Results showed that ensemble learning algorithms outperformed other approaches, with Random Forest (RF) emerging as the top-performing model, achieving the highest validation accuracy of 98.82%. When tested on unseen data, the RF model maintained a comparable accuracy of 98%, demonstrating its robustness, and generalizability in accurately distinguishing between different stages of KC severity.

关键词： Radio frequency Accuracy Machine learning algorithms Computational modeling Scalability Tomography data models Robustness Ensemble learning Random forests

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Emotional Interaction Between Human and Robot

Emotional Interaction Between Human and Robot

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

作者： Ali Nasir Jamilu Umar Yahaya Abdulrazaq Nafiu Abubakar Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Manufacturing and Robotics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

With a goal to encourage task compliance and improve well-being, this research proposes a Markov Decision Process (MDP) model for socially assistive robots (SARs) to enable emotionally meaningful interactions with human users. The model considers the emotional states of the robot as well as the emotional and attentional states of the human user together with the effect of other people in the surroundings, therefore affecting the efficacy of the interaction. An adaptive decisionmaking method is provided by using human attention dynamics and emotional transitions to let the robot dynamically change its emotional state and tactics in real-time. This study focuses primarily on the emotional behavior of the human, the future study will entail the detailed set of actions to be executed by the human. The study incorporates an online learning system to dynamically update transition probabilities, therefore adjusting to fluctuations in human emotional reactions. The model was verified using a simulation-based case study to show its capacity to produce an optimum strategy for persuasive encounters under different environmental settings.

关键词： learning systems Adaptation models Fluctuations Markov decision processes Decision making Human-robot interaction Assistive robots Real-time systems Behavioral sciences Robots

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