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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Certorio, Jair Martins, Nuno C. La, Richard J. Arcak, Murat Institute for Systems Research University of Maryland College Park Department of Electrical and Computer Engineering College ParkMD20740 United States University of California Department of Electrical Engineering and Computer Science BerkeleyCA94720 United States

ISBN: (纸本)9798350316339

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population's strategy profile, thus affecting the ES's state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stability of the ES's equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ESs, such as predator-prey dynamics. The stability of the equilibrium is established with the support of a Lyapunov function, which provides useful bounds on the transient states. © 2024 IEEE.

关键词： Lyapunov functions

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A New Secure Channel Free Public Key Encryption with Keyword Search Scheme Based on ElGamal Cryptosystems

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International Journal of Network Security 2023年第6期25卷 1070-1076页

作者： Hwang, Min-Shiang Hsu, Shih-Ting Yang, Cheng-Ying Department of Computer Science and Information Engineering Asia University Taiwan Fintech and Blockchain Research Center Asia University Taichung 354 Wufeng Taiwan Department of Management Information Systems National Chung Hsing University Taiwan Department of Computer Science University of Taipei Taipei Taiwan

Public key encryption with keyword search (PEKS) scheme enables one to send the trapdoor, which involves the encrypted keyword in querying data without revealing the keyword. The trapdoor should transfer into the secret channel, which is costly and inefficient. Many articles proposed efficient, secure, channel-free public key encryption with keyword search (SCF-PEKS) schemes. After that, many schemes focus on "against the offline keyword guessing attack (OKGA)" by enhancing the model. However, most PEKS/SCF-PEKS schemes are constructed with bilinear pairing. However, the PEKS/SCF-PEKS based on bilinear pairing is susceptible to offline keyword-guessing attacks. In this article, we will propose a new SCF-PEKS based on ElGamal cryptography. The scheme is secure against offline keyword guessing attacks and improves efficiency. © (2023), (Femto Technique Co.). All Rights Reserved.

关键词： Search engines

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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

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computer systems science & engineering 2022年第9期42卷 1095-1107页

作者： Ayman Altameem Jaideep Singh Sachdev Vijander Singh Ramesh Chandra Poonia Sandeep Kumar Abdul Khader Jilani Saudagar Department of Computer Science College of Applied StudiesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering Manipal University JaipurJaipur303007India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

关键词： Machine learning brain signal hand motion recognition braincomputer interface convolutional neural networks

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Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

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computer systems science & engineering 2023年第8期46卷 1879-1900页

作者： Neenavath Veeraiah Youseef Alotaibi Ahmad FSubahi Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaA.P.521180India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

An abnormality that develops in white blood cells is called *** diagnosis of leukemia is made possible by microscopic investigation of the smear in the *** training is necessary to complete the morphological examination of the blood smear for leukemia *** paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support *** proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear *** operations are used to crop the nucleus based on automated *** Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer *** entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the *** proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation *** in WBC imaging characteristics depends on the cytoplasmic and nuclear *** computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear *** classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression *** designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the *** proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape *** exhibits a higher classification accuracy value of 99.6%when combined with the other *** comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

关键词： White blood cells leukemia segmentation histogram blood smear

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Intrusion Detection Mechanism Using Deep Learning 5

Intrusion Detection Mechanism Using Deep Learning

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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Optimizing Facial Recognition: An Analytical Comparison of Traditional and Deep Learning Approaches 7

Optimizing Facial Recognition: An Analytical Comparison of T...

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7th International Conference on Data science and Its Applications, ICoDSA 2024

作者： Sakib, Shadman Siddiky, Md Nurul Absar Arifuzzaman, Md. Chowdhury, Md. Jalal Uddin University of Maryland Baltimore County Department of Information Systems Maryland21250 United States University of North Carolina at Charlotte Department of Electrical and Computer Engineering Charlotte United States Leading University Sylhet Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350365351

In the rapidly evolving domains of AI and Internet tech, face recognition, a key machine learning application, is increasingly used in security, identity verification, and public monitoring. As this technology progresses, its applications are expanding. A critical factor in its growing popularity is the advancement of the underlying algorithms, which drives its effectiveness in various applications. This paper investigates facial recognition technologies using the Olivetti faces dataset. It compares the efficacy of Principal Component Analysis (PCA), Eigenface method, Support Vector Machine (SVM) with linear kernel, a standard Convolutional Neural Network (CNN), and a CNN-Transfer Learning approach with MobileNet. The transfer learning is trained with the Adam optimizer (learning rate 0.0004) and categorical cross entropy loss. Over 100 epochs with a batch size of 64, the model's performance, in terms of accuracy and loss, is meticulously tracked and visualized, providing insights into its learning progression. The final evaluation includes a detailed classification report, highlighting precision, recall, and F1-scores, and these metrics are graphically represented for a comprehensive understanding of the model's performance. The CNN model achieved 80% accuracy, while PCA and SVM exhibited high accuracies, reaching 91% and 97.5%, respectively. We have also applied the eigenface method and achieved 91% accuracy in face recognition. Notably, the CNN-Transfer Learning method demonstrates superior performance with a 95% accuracy, highlighting the potential of deep learning in facial recognition applications. © 2024 IEEE.

关键词： Contrastive Learning

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Directed Machine Learning Approaches for Determining Alzheimer Disease 3

Directed Machine Learning Approaches for Determining Alzheim...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Vashist, Ansh Jain, Tarun Sharma, Vijay Shankar Manipal University Jaipur Department of Computer Science and Engineering India Manipal University Jaipur Department of Iot & Intelligent Systems India

ISBN: (纸本)9798350319125

Alzheimer's is a neurogenic disease which progress into neurological disorder that primarily affects cognitive function and memory. It's a Neurodegenerative (ND) disease, characterized by the gradual deterioration of cognitive function, memory, thinking, and behaviour. There are two most common diseases among neurodegenerative diseases: (a) Alzheimer's Disease and (b) Parkinson's Disease. We used 12 classifiers on the given dataset on UC Irvine Machine Learning Repository. The machine learning algorithms were engaged to identify Alzheimer Disease. Our research results showed that the XGB Model is the one that shows the best accuracy, of 100%, of all the 12 classifiers. © 2023 IEEE.

关键词： AdaBoost Model Alzheimer's ANN Model Binary Decision Tree Model Classification Complement Naive Baye Model Disease Detection Ensemble Voting Model Gaussian Naive Baye Model Logistic Regression Machine Learning Multinomial Naive Baye Model Random Forest SGD Model XG Boost Model

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