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Microwave photonics instantaneous frequency measurement scheme assisted by machine learning method 4

Microwave photonics instantaneous frequency measurement sche...

引用

4th Optics Frontier conference, OFS 2024

作者： Liu, Shuoyan Chen, Dan Cui, Tianci Zhang, Yunxia School of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang050043 China

ISBN: (纸本)9781510682269

this study proposes a microwave photonics instantaneous frequency measurement (IFM) system assisted by machine learning, employing a Polarization-division multiplexing dual-parallel Mach-Zehnder modulator (PDM-DPMZM) as the primary modulation device. the system utilizes the periodic attenuation of dispersion-compensating fibers (DCF) to establish an intensity ratio function (ACF), enabling large-range and high-precision frequency measurement. Additionally, the Stacking ensemble learning method from machine learning is employed to train and test stimulation data obtained from the measurement system, further enhancing the accuracy of frequency measurement. the simulation results indicate that the proposed IFM system achieves an error within 20MHz, removing a small amount of abnormal data, within the frequency range of 4.5GHz to 20GHz. this study provides a novel approach and methodology for the design and optimization of microwave photonics IFM systems, offering practical application value. © 2024 SPIE.

关键词： Microwave photonics

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Quantum-Inspired Neural Network on Handwriting Datasets 4th

Quantum-Inspired Neural Network on Handwriting Datasets

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4th international conference on Trends in Cognitive Computation Engineering, TCCE 2022

作者： Shah, Manik Ratna Sarraf, Jay Pattnaik, Prasant Kumar Bandyopadhyay, Anjan School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India

ISBN: (纸本)9789811994821

With the advancement of machine learning for classifying and categorizing larger sets of data, there is a high need for greater computational power. Quantum computing in machine learning advances to solve this in a lesser time, consuming less energy and power. this paper includes the comparative analysis of Traditional Neural Networks with Quantum Neural Networks using handwritten digits from the simplified version of the MNIST dataset. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Detection and Mitigation of DDoS Attacks in SDN Using machine learning (ML) 4

Detection and Mitigation of DDoS Attacks in SDN Using Machin...

引用

4th international conference on Digital Age and Technological Advances for Sustainable Development, ICDATA 2023

作者： Tahirou, Abdoul Karim Konate, Karim Soidridine, Moussa Moindze Université Cheikh Anta Diop Departement Math Informatique Dakar Senegal

ISBN: (纸本)9798350311112

the Software Defined Network (SDN) is a new network paradigm that has been growing in recent years because of its flexibility and ease in managing and operating computer networks. In SDN one of the advantages is the separation of the data plane from the control plane, unlike the traditional network where the data plane and the control plane are integrated together in the same device. However, SDN is facing a severe security issue today. One of the biggest threats to SDN security is distributed denial of service (DDoS). these DDoS attacks can take place across all SDN layers and communication channels. Several techniques are used for the detection, prevention, and mitigation of DDoS attacks in SDN in general but more specifically on the controller. However, one of the most popular techniques used in recent years to detect DDoS attacks is machine learning. It is in this context that we will use supervised machine learning algorithms such as K-Nearest Neighbors (K-NN), Support Vector machine (SVM), and Naive Bayes (NB). these algorithms will be trained on the CIC-2019 dataset. Finally, a real-time SDN simulation environment composed of Mininet, Ryu controller, and SNORT NIDS is used to evaluate the effectiveness of these algorithms. Once the attack is detected, the attacker's IP address and port number are blocked and recorded in a blacklist for mitigation. © 2023 IEEE.

关键词： Support vector machines

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Text Analysis Based Human Resource Productivity Profiling 4th

Text Analysis Based Human Resource Productivity Profiling

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Pradhan, Basudev Rautaray, Siddharth Swarup Panda, Amiya Ranjan Pandey, Manjusha School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9783031488870

Email being an efficient, cost-effective, real-time communication mode results into effective productivity among the professional in the organization. It constitutes almost 90% of daily office procedures in organizations, hence the productivity of organizations depends heavily on the text communicated in emails. the presented research work focuses on email profiling in organizations based on mail text interpretation and analysis. In the proposed work we will be working on datasets containing email communication of ENRON Corporation as test case. the profiling would be done using Text interpretation and analysis algorithm using machine learning algorithms. the BoW will be implemented to analyze and predict the characteristics of incoming and outgoing emails, then these could be mapped and profiled as per the behavior of employees into 3 categories of productive based on positive responses, neutral and non-productive based on negative responses. © 2024, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Optimising Lung Cancer Screening through Advanced Techniques for Image Analysis: An Efficient Deep learning Approach 4

Optimising Lung Cancer Screening Through Advanced Techniques...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sherin, K. Jaspin, K. Harini, S. Sivanantham, Sakthivel Senthilvelan, Surendar theerthagiri, Srinivasan St.Joseph's Institute of Technology Chennai600119 India Chennai600119 India

ISBN: (纸本)9798350300888

the objective of this analysis is to identify and classify diverse types of lung cancer and propose an improved system to enhance the current best approach. the proposed system utilizes a preprocessed dataset to train and test various types of lung cancer, including Lung Adenocarcinoma, Lung Large Cell Carcinoma, and Squamous Cell Carcinoma, as well as a category for normal lungs. the accuracy of the detection methods is evaluated using a confusion matrix to ensure maximum accuracy. the detection methods for lung cancer are rated based on their accuracy in detecting the disease. this study emphasizes the significance in detecting abnormal lung cells indicative of cancer as expeditiously as possible to alleviate mortality rates and highlights the importance of computer-aided diagnosis in improving accuracy. the proposed approach achieved an impressive accuracy of 93.11%. © 2023 IEEE.

关键词： computer aided diagnosis

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Self-Organizing Clustering for Federated learning 4

Self-Organizing Clustering for Federated Learning

引用

4th international conference on computers and Artificial Intelligence Technology, CAIT 2023

作者： Esmaeili, Babak Derakhshandeh, Zahra Zhao, Ming Arizona State University Computer Science Cidse Tempe United States California State University Department of Computer Science Hayward United States

ISBN: (纸本)9798350326710

the emergence of widely utilized IoT devices and the vast amount of data they host provide excellent candidates for machine learning and deep learning applications. However, the distributed nature of these devices, the growing emphasis on user privacy, the sensitivity of users' and organizations' data, as well as regulations that prohibit data transfer to centralized data centers, have necessitated the adoption of new privacy-preserving machine learning techniques. Federated learning, a recently introduced distributed paradigm to train a model in a distributed system of individual user devices, has emerged in response to such essential needs. Within this study, we present a new architecture framework for federated learning that aims to mitigate the risk of bottlenecks and facilitate learning in a more distributed manner than the conventional central-server-based approach. By employing our fully decentralized clustering algorithm, nodes present in the network assume the roles of aggregating servers or trainers in an adaptive self-organizing manner. through a series of experimental results, leveraging our clustering algorithm, we established two different two-layer federated learning frameworks and compared their performances with traditional centralized and fully decentralized federated learning approaches. the results unequivocally demonstrate the promise of the proposed algorithm, showcasing its potential without significantly compromising accuracy. this innovative framework introduces a distributed learning opportunity for federated learning, mitigating communication volume on the server and reducing the risk of network failures or bottlenecks. Our analysis also reveals a remarkable reduction in message exchange among network entities, presenting a substantial improvement over decentralized federated learning. © 2023 IEEE.

关键词： Clustering algorithms

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Modeling Inverse Demand Function with Explainable Dual Neural Networks 23

Modeling Inverse Demand Function with Explainable Dual Neura...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Cao, Zhiyu Chen, Zihan Mishra, Prerna Amini, Hamed Feinstein, Zachary Stevens Inst Technol Hoboken NJ 07030 USA Georgia State Univ Atlanta GA 30303 USA Univ Florida Gainesville FL USA

ISBN: (纸本)9798400702402

Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to proliferate across a broad spectrum of seemingly unrelated entities. Price impacts are currently modeled via exogenous inverse demand functions. However, in real-world scenarios, only the initial shocks and the final equilibrium asset prices are typically observable, leaving actual asset liquidations largely obscured. this missing data presents significant limitations to calibrating the existing models. To address these challenges, we introduce a novel dual neural network structure that operates in two sequential stages: the first neural network maps initial shocks to predicted asset liquidations, and the second network utilizes these liquidations to derive resultant equilibrium prices. this data-driven approach can capture both linear and non-linear forms without pre-specifying an analytical structure;furthermore, it functions effectively even in the absence of observable liquidation data. Experiments with simulated datasets demonstrate that our model can accurately predict equilibrium asset prices based solely on initial shocks, while revealing a strong alignment between predicted and true liquidations. Our explainable framework contributes to the understanding and modeling of price-mediated contagion and provides valuable insights for financial authorities to construct effective stress tests and regulatory policies.

关键词： explainable machine learning inverse demand function financial contagion asset liquidation FinTech deep learning

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Analyze and Forecast the Cyber Attack Detection Process using machine learning Techniques 4

Analyze and Forecast the Cyber Attack Detection Process usin...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Deepak, Nrusimhadri Sai Hanitha, Tanneru Tanniru, Kiranmai Kiran, Lukka Raj Sai, N.Raghavendra Jogendra Kumar, M. Koneru Lakshmaih Education Foundation Department of CSE AP Vaddeswaram India

ISBN: (纸本)9798350300093

One of the most crucial global concerns is the issue of cybercrime, which leads to significant financial losses for nations and their citizens every day. the frequency of cyberattacks has steadily increased, emphasizing the need to identify the individuals behind these criminal activities and understand their strategies. Detecting and preventing cyberattacks pose significant challenges, but recent advancements have introduced security models and prediction tools based on artificial intelligence to tackle these issues. Although there is a wealth of literature on crime prediction strategies, they may need to be more effectively suited for awaiting cybercrime and cyber-attack techniques. One potential solution to address this problem involves utilizing real-world data to determine the occurrence of an attack and identify the responsible party. this information encompasses details about the offense, offender demographics, property damage, and attack vectors. Forensic teams can collect information from victims of cyber-attacks through application processes. this research study employs machine learning techniques to analyze cybercrime using two models and predict how the attributes can contribute to identifying the method of cyber-attack and the criminal. this study has compared eight different machine-learning techniques, and discovered that they yielded similar results in terms of accuracy. the Support Vector machine (SVM) linear model achieved the highest accuracy rate among the various cyber-attack methods tested. In the first model, valuable insights on the types of attacks victims were likely to face. Logistic regression, with a high success rate, was the most effective strategy for identifying malicious actors. the second model focused on comparing offender and victim attributes to make predictions regarding identification. Our findings indicate that the likelihood of becoming a victim of cyberattacks decreases with higher levels of education and wealth. this propo

关键词： Support vector machines

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Enhancing Forest Fire Classification with Feature Selection and machine learning Based on PRISMA Hyperspectral Data 4th

Enhancing Forest Fire Classification with Feature Selection ...

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4th international conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Meroufel, Hanifi Berrichi, Faouzi Amri, Mohammed Bilel Larabi, Mohammed El Amin Ctr Tech Spatiale Agence Spatiale Algerienne Arzew Algeria Algerian Space Agcy Algiers Algeria

ISBN: (纸本)9783031821554;9783031821561

Forest fires, a significant global threat causing vast annual devastation where each year millions of hectares are destroyed over the world. Burned areas necessitate early and accurate detection for effective response. this study investigates the application of Feature Selection (FS) to address data complexity in forest fire detection utilizing PRISMA hyperspectral imagery (HSI). FS is employed to select the most relevant information, thereby reducing complexity and facilitating the interpretation of results. the effectiveness of Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LGBM), Random Forest (RF), and Support Vector machine (SVM) algorithms is investigated. Analysis reveals that while the raw 229-band data yielded a reasonable accuracy with RF, applying FS through wrapper methods only select 13 bands and improve the performance of LGBM, XGBoost, and SVM. Notably, the best model achieves a classification precision of 94% and recall of 91%, demonstrating the effectiveness of FS in enhancing forest fire detection and mapping.

关键词： Forest fires Feature selection XGBoost LGBM Random Forest Support Vector machine

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Predictive Power of machine learning in Chronic Disease: A Comprehensive Review and Meta-Analysis 4

Predictive Power of Machine Learning in Chronic Disease: A C...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Rani, Akanksha Garg, Shivangi Sharma, Astha Kumar, Ashwni Indira Gandhi Delhi Technical University for Women Department of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798350300888

Chronic diseases pose a significant global health challenge, affecting over a quarter of adults and placing a big burden on healthcare systems and individuals. the advent of the 'Smart Healthcare' era and edge cut technologies opened up new possibilities for managing chronic diseases. this article focuses on chronic diseases with a high risk of development, including lung carcinoma, breast cancer, colorectal cancer, gastrointestinal cancer, liver cancer, cataracts, and cardiovascular-cerebrovascular diseases. the rates of these diseases were found to be 57.85%, 74.63%, 22.13%, 41.65%, 53.39%, 20.24%, and 83.94%, respectively. the study discusses the implications of these findings and underscores the importance of leveraging advanced technologies for effective management and prevention of chronic diseases. © 2023 IEEE.

关键词： Diseases

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