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A Variable Step-Size Hybrid Adaptive Nonlinear Filter for Solar Radiation Prediction

A Variable Step-Size Hybrid Adaptive Nonlinear Filter for So...

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International Conference on systems and Informatics (ICSAI)

作者： Y. Xiao L. Ma K. Khorasani Dept. of Management & Information Systems Prefectural University of Hiroshima Hiroshima Japan Dept. of Electrical & Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In this paper, a new hybrid adaptive nonlinear filter (HANF) scheme with variable step sizes (VSS) is proposed for solar radiation prediction. Our methodology consists of a Volterra filter and a functional link artificial neural network (FLANN) filter. The Volterra filter with the first- and second-order kernels is included, that is capable of expressing both linearity and nonlinearity. The FLANN filter can handle the nonlinearity that may be expressed by higher-order exponential terms that the Volterra filter with a limited number of kernels is unable to deal with. The VSSs are introduced in the two filters to allow the HANF to enjoy desirable tracking capability such that the time-varying nonlinearity underlying the nonlinear phenomena can be detected and tracked. The proposed VSS-HANF is applied to a real hourly solar radiation time sequence to confirm its improved prediction performance as compared to its counterpart with fixed step sizes.

关键词： Accuracy Finite impulse response filters Adaptive filters Weather forecasting Linearity Nonlinear filters Artificial neural networks Solar radiation Kernel Informatics

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Email Armour: A Multi-Layered Email Defense Solution

Email Armour: A Multi-Layered Email Defense Solution

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International Conference on information Technology Research (ICITR)

作者： K.T.A.U Lakmal L.M.C Perera S.P.K Padmika S.P.A De Silva Dinithi Pandithage Deemantha Siriwardana Dept. computer systems engineering Sri Lanka institute of information technology Malabe Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Phishing and spam emails are evolving threats in today's digital landscape, posing significant risks to small and medium-sized businesses (SMBs). Despite the growing demand for email security solutions, SMBs often face barriers such as high costs and complexity. To address this gap, this study introduces a cost-effective, multi-layered email security solution leveraging machine learning. The proposed system comprises four core components: (1) spam detection using natural language processing (NLP) to analyze email content, (2) phishing URL and QR code detection by identifying malicious patterns and attributes, (3) attachment security analysis to detect malware and harmful scripts, and (4) suspicious URL detection focusing on JavaScript obfuscation. The model was trained and validated on publicly available datasets, achieving an overall accuracy of 85% and demonstrating effectiveness in identifying diverse email threats. Designed as an email client, this tool empowers SMBs to secure communication with an accessible and robust defense against email-based attacks

关键词： Uniform resource locators Unsolicited e-mail Phishing Focusing Machine learning QR codes Natural language processing Malware security information technology

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An Approach for Improving the network Resilience Based on Optimal Structural Reservation 3

An Approach for Improving the Network Resilience Based on Op...

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3rd International Conference on Communications, information, Electronic and Energy systems, CIEES 2022

作者： Kotenko, Igor Saenko, Igor Lauta, Oleg Bagretsov, Sergey Laboratory of Computer Security Problems St. Petersburg Russia Admiral Makarov State University of Maritime And Inland Shipping Dept. of Integrated Information Security St. Petersburg Russia St. Petersburg Space Academy Dept. of Information And Telecommunication Systems St. Petersburg Russia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

Nowadays, increasing the resilience of information and telecommunication networks in the face of many different threats and impacts is one of the most important areas of network security. The task of optimal structural reservation of the telecommunication network is formulated as a search for a reservation option, which ensures maximum resilience of the network when meeting the cost constraints of the reservation network elements. The paper describes the problem of optimal structural reservation for improving the network resilience. Possible solutions of this problem are considered. It is shown that the preferred method to solve the problem is the method of Lagrange multipliers. Results of experimental evaluation of proposed method are presented. © 2022 IEEE.

关键词： Lagrange multipliers

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EMDA-Net: Earth Mover’s Distance (EMD) influenced Attention-aided Neural network for Medical Image Classification

EMDA-Net: Earth Mover’s Distance (EMD) influenced Attention...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Surya Majumder Kushaj Mallick Wrick Pal Somenath Chakraborty Ram Sarkar Dept. of Computer Science & Engineering Heritage Institute of Technology Kolkata India Dept. of Electrical Engineering Jadavpur University Kolkata India Dept. of Computer Science & Information Systems West Virginia University Institute of Technology West Virginia USA Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

Medical image analysis methods need to be more generalized in order to meet the substantial problems associated with cancer detection and treatment, including skin, cervical, and breast cancer. In this work, we propose a novel lightweight convolutional neural network (CNN) architecture, named EMDA-Net (Earth Mover’s Distance (EMD) influenced Attention-aided Neural network), specifically designed to meet the requirements of cancer subtype classification. It is built on the MobileNetV2 backbone and is equipped with hybrid statistical mechanism-aided attention mechanisms. Our model ensures high-performance while maintaining a lightweight design by leveraging the capabilities of the MobileNetV2 architecture and the attention mechanism. To improve similarity detection across data channels, we present a novel EMD influenced attention technique. We have examined datasets related to skin, cervical, and breast cancer for experimentation. Our model especially outperforms the state-of-the-art techniques, demonstrating its effectiveness in maximizing diagnosis precision for various cancer types across the datasets. The code of the proposed model will be made public at: https://***/q5DlN

关键词： Earth Attention mechanisms Codes Neural networks Skin Breast cancer Convolutional neural networks Medical diagnostic imaging Machine intelligence Image classification

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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2024 IEEE International Conference on E-Health networking, Application and Services, HealthCom 2024

作者： Panchal, Barkha Bhatia, Jitendra Kumhar, Malaram Tanwar, Sudeep Dutta, Ashit Kumar Rodrigues, Joel J.P.C. Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AlMaarefa University Saudi Arabia Amazonas State University AM Manaus Brazil

ISBN: (纸本)9798350350548

The Internet of Medical Things (IoMT) revolutionizes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and KNN is 84.3%. The results shows that KNN performs better than other models by the factor of 9%. © 2024 IEEE.

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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Predicting Customer Behavior Using Neural network Optimized by Grasshopper Algorithm 2

Predicting Customer Behavior Using Neural Network Optimized ...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Loaeibi, Zoheir Alqammaz, Abdullah Sara, Massar Boostani, Reza School of Engineering Shiraz University Electrical and Computer Engineering Faculty Cse and It Dept. Shiraz Iran Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Digital marketing, as an essential branch of science, could provide value for commercial organizations and strengthen the interaction with customers via increasing the electronic services. In digital marketing, real-time analysis of customer behaviors is considered as a key program, which could encourage novel advertisement concentration. Each deal that is assigned to a costumer, is a part of individual behavior and is a sample of feature vectors based on a set of interactions is modelled for creating the customer behavior. In this research, using data mining techniques and metaheuristic algorithms, the patterns of customer behavior are identified and predicted. Neural network as a classifier with acceptable performance is used for complex tasks. In the train phase, neural network is usually worked with back propagation algorithm. This algorithm suffers from falling into the local optima and needs to adjust the weights and using proper initialization weights. To optimize both weights and structure of the neural network, we adopt grasshopper evolutionary algorithm to adjust the weight in an iterative manner along with the adjusting the number of neurons in the hidden layer. In the proposed method, the probability of falling in the local optima is reduced and also the suggested algorithm could provide acceptable performance compared to the other rivals in a rich benchmark of digital marketing. © 2023 IEEE.

关键词： Optimization

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Trusted Access to 6G Testbeds Through a security Intent-Driven Software-Defined Perimeter Framework 29

Trusted Access to 6G Testbeds Through a Security Intent-Driv...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and networks, CAMAD 2024

作者： Fernandez-Martinez, Carolina Bikos, Anastasios Verikoukis, Christos Siddiqui, Shuaib i2CAT Foundation Barcelona08034 Spain Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona08018 Spain Iquadrat Informatica Sl Barcelona08006 Spain Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona08034 Spain Industrial Systems Institute Athena Research Center Patras26504 Greece University of Patras Dept. of Computer Engineering and Informatics Patras26504 Greece

ISBN: (纸本)9798350377644

Parallel to the evolution of recent trends in the cybersecurity industry and the increase of cyberattacks in the last few years, there is renovated interest on the application of software-defined techniques to enforce best security practices, such as in the Software-Defined Perimeter specification and the Zero Trust architectures. This work proposes a software-defined network segmentation and access control framework aligned with these principles. This solution captures minimal security requirements from testbed operators to establish ad-hoc, isolated connectivity and access control for end-users (experimenters) interacting with exposed endpoints from the exposed services in the cloud testbeds. The elicited architecture, design decision and initial implementation are introduced. © 2024 IEEE.

关键词： Testbeds

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19

Investigating Online Searching Behavior Based on Google Tren...

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Frontiers in Education (FIE) Conference

作者： Abdelkader Nasreddine Belkacem Nuraini Jamil Saed Alrabaee Dept. of Computer and Network Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Computer Science and Software Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Information Systems and Security College of IT UAE University Al Ain United Arab Emirates

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning.

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High-Speed CBJT Operational Amplifier Based on “Folded” Cascode with a Differentiating Transient Correction Circuit

High-Speed CBJT Operational Amplifier Based on “Folded” Ca...

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Wave Electronics and its Application in information and Telecommunication systems (WECONF)

作者： A. E. Titov D. V. Kuznetsov D. Y. Denisenko I. A. Alferova dept. of Automatic Control Systems Southern Federal University Rostov-on-Don Russia dept. of Scientific Research dept. of Information Systems and Radio Engineering Don State Technical University Rostov-on-Don Russia dept. of Scientific Research Don State Technical University Rostov-on-Don Russia dept. of Scientific Research dept. of Cyber Security of Information Systems Don State Technical University Rostov-on-Don Russia

ISBN: (数字)9798350364989

ISBN: (纸本)9798350364996

A circuit design of a high-speed CBJT operational amplifier (OpAmp) based on an “folded” cascode is proposed, in which a differentiating transient correction circuit is introduced to increase the maximum slew rate of the output voltage (SR). The considered OpAmp is intended for use in a subclass of so-called discrete-to-analog SC filters on switched capacitors, where (in some important cases) higher SR values of the OpAmp are required. In addition, such high-speed OpAmps are in demand in drivers of high-speed analog-to-digital converters and other devices of automatics and computer engineering. computer simulation in LTSpice environment shows that the proposed CBJT OpAmp circuit designs for micron silicon technological processes provide SR increase from 45.8 V/μs to 18000 V/μs.

关键词： Operational amplifiers Micrometers computer simulation Voltage Switches Silicon Circuit synthesis

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