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European Conference on Microwave

作者： Kai Xu Yishan Wang Wei Gong Jayakrishnan Methapettyparambu Purushothama Chaoyun Song George Goussetis Yuan Ding The Institute of Sensors Signals and Systems Heriot-Watt University U.K Institute for Digital Communication University of Edinburgh U.K. School of Computer Science and Technology and the School of Data Science University of Science and Technology of China China Department of Engineering Strand Campus King’s College London U.K.

ISBN: (数字)9782874870774

ISBN: (纸本)9798350385892

Backscatter communication (BackCom) emerges as a promising solution for the Internet of Things (IoT) ecosystem due to its low-power operation, which provides a potential for power autonomy through radio frequency (RF) energy harvesting (EH) technology. However, the dynamic power allocation tuning between communication and powering is still a challenge. To overcome it, in this paper we propose an agile power allocation strategy by integrating the EH circuit into backscatter modulator, i.e. employing it as one of the load elements in the backscatter tag. The power allocation can thus be dynamically adjusted in the time domain, utilizing a square wave with variable duty cycles to fine-tune the power ratio digitally. Our approach balances the backscatter system in communication and powering, providing a scalable solution adaptable to most existing BackCom systems.

关键词： Radio frequency Power dividers Modulation Microwave communication Resource management Internet of Things Energy harvesting Time-domain analysis Tuning Backscatter

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Towards a Theoretical Framework of Autonomous systems Underpinned by Intelligence and systems sciences

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

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Identifying Application-Level Distributed Denial-of-Service (DDoS) Attacks with Machine Learning (ML) 3rd

Identifying Application-Level Distributed Denial-of-Service ...

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3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Yaqub, Muhammad Atif Mustafa, Muzzamil Chuhan, Saad Hussain Sattar, Basit Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Qaiser, Muhammad Saad Abdin, Zain ul Bilal, Afshan Ali, Jibran Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Artificial Intelligence University of Management and Technology Lahore Pakistan Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan Department of Computer Science Bahria University Lahore Campus Lahore Pakistan Premier Systems Karachi Pakistan

ISBN: (纸本)9783031863042

The modern digital age has seen an exponential growth in internet penetration, which in turn has increased the probability of a denial-of-service attack. When an evil user launches a Denial of Service (DoS) attack, it’s with the goal of flooding computing and network resources and blocking access to them for normal users. These kind of assaults may happen at any level of the OSI model, from the application layer all the way down to the transport layer. This research focuses on application layer DoS attacks instead of transport and network DoS attacks and attempts to detect Denial of Service using Machine Learning and Neural Network approaches. An updated dataset of Denial of Service attacks divided into many subcategories was used in this experiment. According to the research, the decision tree approach performed well in terms of accuracy and splitting compared to the logistic regression approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Logistic regression

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Internet of things and optimized knn based intelligent transportation system for traffic flow prediction in smart cities

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Measurement: Sensors 2024年 35卷

作者： Mrudula, Sunkara Teena Meenakshi Ritonga, Mahyudin Sivakumar, S. Jawarneh, Malik F, Sammy Keerthika, T. Rane, Kantilal Pitambar Roy, Bhaskar Velagapudiramakrishna Siddhartha engineering College Andhra Pradesh Vijayawada India Apeejay Stya University Haryana Sohna India Universitas Muhammadiyah Sumatera Barat Indonesia Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Avadi Tamilnadu Chennai India College of Computer Science and Informatics Amman Arab University Jordan Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of AIML Asansol Engineering College Asansol India

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595. © 2024 The Authors

关键词： Traffic congestion

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Implementing Deep Learning Techniques into Healthcare System Improvement

Implementing Deep Learning Techniques into Healthcare System...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Harmeet Kaur Kochhar Mrudula Kilaru Nasser Aljohani Himanshu Rai Goyal Devendra Singh DilipKumar Jang Bahadur Saini University Institute of Media Studies Chandigarh University Velagapudi Ramakrishna Siddhartha Engineering College Information Systems Department Islamic University of Madinah Madinah Saudi Arabia Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Computer Science & Engineering Sawmi Rama Himalayan University Dehradun India

Artificial intelligence (AI) has the potential to transform clinical decision-making and assistance. In any case, the utilization of current AI methods remains only as a supportive solution to make clinical decisions. The existing methodologies fail to perform in-depth analysis to deliver accurate predictions. This study reviews the integration of advanced technologies AutoML in healthcare sector by considering the data privacy, heterogeneity and interpretability. This review will be a useful resource for medical practitioners interested in applying information science methods to their field of study.

关键词： Technological innovation Renewable energy sources Medical services Transforms System improvement Writing Software

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Data clustering: an essential technique in data science

arXiv

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arXiv 2024年

作者： Dinh, Tai Hauchi, Wong Lisik, Daniil Koren, Michal Tran, Dat Yu, Philip S. Torres-Sospedra, Joaquín The Kyoto College of Graduate Studies for Informatics 7 Tanaka Monzencho Sakyo Ward Kyoto Kyoto City Japan University of Gothenburg Medicinaregatan 1F Göteborg413 90 Sweden Shenkar College of Engineering Design and Art Anne Frank St 12 Tel Aviv Ramat Gan Israel University of Canberra 11 Kirinari St BruceACT2617 Australia Department of Computer Science University of Illinois at Chicago Chicago United States Department of Computer Science University of Valencia Valencia Spain

This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering’s transformative potential. The paper concludes with insights into future research directions, emphasizing clustering’s role in driving innovation and enabling data-driven decision-making. © 2024, CC BY.

关键词： Adversarial machine learning

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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Spatial Attention Cooperative-Based Graph Convolutional Model for Person Re-Identification

SSRN

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SSRN 2023年

作者： Xiong, Mingfu Xiong, Jiefan Wang, Zhongyuan Chen, Jia Hu, Ruimin Ser, Javier Del Muhammad, Khan Xiong, Zixiang School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of Derio48160 Spain Bilbao48013 Spain The Department of Electrical and Computer Engineering Texas A&M University College StationTX7784 United States

Recently, person re-identification (person Re-ID) equipped with an attention mechanism has gradually emerged as one of the most active areas of study in the field of computer vision and neural processing. Despite the upsurge in related research, existing attention-based methods still face two major challenges when recognizing different persons with similar appearances in unpredictable realistic environments: (1) the adaptability of a single local salient feature to hostile scenes cannot be guaranteed, and (2) most existing methods originating from attention features usually find a simple linear transformation or implement variation strategies, which makes it difficult to discriminate different pedestrians with similar appearance attributes. To address these issues, this paper proposes a spatial attention cooperative-based graph convolutional model (SACGCM) to examine the relevance of local body regions for individuals. The proposed framework encompasses a multi-dimensional attention model (MAM) and spatial adaptive graph convolutional model (SAGCM). In particular, the MAM comprises a dual-attention (local and global) module and feature channel-attention stage, which are used to capture multiple key points for each person. Then, SAGCM is used to construct spatial nonlinear transformation relationships among the various body regions for each person using graph optimization. Extensive experiments were conducted using public person Re-ID datasets (Market-1501, DukeMTMC-reid, and CUHK-03). The results demonstrate that the performance of the proposed approach is superior to that of state-of-the-art methods by between 2 and 10\% at Rank-1 (mAP). © 2023, The Authors. All rights reserved.

关键词： Linear transformations

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E-commerce customer churn prevention using machine learning-based business intelligence strategy

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Measurement: Sensors 2023年 27卷

作者： J, Shobana Gangadhar, Ch. Arora, Rakesh Kumar Renjith, P.N. Bamini, J. Chincholkar, Yugendra devidas Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Electronics & Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada520007 India Department of Information Technology Krishna Engineering College Mohan Nagar UP Ghaziabad India Computer Science and Engineering Department Vellore Institute of Technology Chennai India Sri Ramakrishna College of Arts & Science Tamil Nadu Coimbatore India Department of Electronic & Telecommunication Sinhgad College of Engineering Pune India

Businesses in the E-Commerce sector, especially those in the business-to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. The best course of action in this circumstance is to detect prospective churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Each customer's information, including searches made, purchases made, frequency of purchases, reviews left, feedback is given, and other data, is kept on file by the e-commerce company. Machine learning and data mining may be aided by examining this enormous quantity of data, analysing customer behaviour, and seeing potential attrition opportunities. The support vector machine is a popular supervised learning method in machine learning applications. Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-Commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle. © 2023 The Authors

关键词： Support vector machines

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Identification of Nonlinearity and Chaos Characteristics in Observational Time Series as an Element of Machine Learning

Identification of Nonlinearity and Chaos Characteristics in ...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： A. Makshanov A. Musaev D. Grigoriev Department of Computing Systems and Computer Science Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences Saint-Petersburg Russia Center for econometrics and business analytics (CEBA) St. Petersburg State University (SPBU) Saint-Petersburg Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

Improving the efficiency of machine learning in monitoring nonstationary processes requires an initial stage of identifying the dynamic and statistical structure of observational time series, which form the training dataset. This step is particularly crucial in artificial intelligence systems that interact with control objects embedded in unstable environments. This study introduces novel algorithms for estimating nonlinearity and chaos indicators within a sliding window framework. The proposed estimation system constructs a generalized portrait of a segment of the observational series. The evolution of these estimates enables the detection of regions exhibiting “flicker noise,” which serves as an indicator of impending abrupt changes in the process state. Essentially, this research presents new methodologies for forecasting, segmenting nonstationary processes, and the early detection of potential structural shifts. From a machine learning perspective, the core objective is to generate generalized portraits of state vector dynamics for applications such as clustering and case-based analysis. In the context of multidimensional measurement series, the study proposes applying a similar approach to their singular projections.

关键词： Chaos Training Current measurement Time series analysis Estimation Machine learning Fractals Vectors Time measurement Monitoring

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