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Enhanced Quantum Convolutional Neural Network for Signature Authentication in Consumer Products

IEEE Transactions on Consumer Electronics 2024年

作者： Raghupathy, Bala Krishnan Vairavasundaram, Subramaniyaswamy Ganesan, Manikandan Namachivayam, Rajesh Kumar Kotecha, Ketan Herencsar, Norbert SASTRA Deemed University Department of CSE India Vellore Institute of Technology School of Computer Science and Engineering Vellore632 014 India SASTRA Deemed University School of Computing India Symbiosis Centre for Applied Artificial Intelligence Pune India Brno University of Technology Faculty of Electrical Engineering and Communication Department of Telecommunications Technicka 3082/12 Brno61600 Czech Republic

Product tracking applications utilize the Internet of Things and cyber-physical systems to identify permitted or unauthorized user intrusions into the system. Classical machine learning algorithms cannot detect every risk in an environment that evolves constantly and where new abnormalities are visible. This article investigates the potential of quantum machine learning (QML) for real-time product purchase monitoring and intrusion detection using an enhanced quantum convolutional neural network (EQCNN) with signature-based detection over a massive volume of search space data (qubits). We suggest a three-stage technique to effectively handle the sensitive content: Pre-processing, EQCNN-based feature extraction, and syntactic pattern recognition. Signature-based identification is a feature of the EQCNN architecture that helps detect particular patterns linked to goods purchases or invasions. The model can minimize product tracking mistakes by utilizing the QML-based EQCNN with signature-based detection, resulting in a more efficient supply chain. © 2024 IEEE.

关键词： Convolutional neural networks

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Further Investigations on Nonlinear Complexity of Periodic Binary Sequences

arXiv

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arXiv 2024年

作者： Yuan, Qin Li, Chunlei Zeng, Xiangyong Helleseth, Tor He, Debiao Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University Hubei Wuhan430062 China Department of Informatics University of Bergen BergenN-5020 Norway Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430072 China

Nonlinear complexity is an important measure for assessing the randomness of sequences. In this paper we investigate how circular shifts affect the nonlinear complexities of finite-length binary sequences and then reveal a more explicit relation between nonlinear complexities of finite-length binary sequences and their corresponding periodic sequences. Based on the relation, we propose two algorithms that can generate all periodic binary sequences with any prescribed nonlinear complexity. © 2024, CC BY.

关键词： Binary sequences

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Kink movement on a periodic background

arXiv

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arXiv 2024年

作者： Dobrowolski, Tomasz Gatlik, Jacek Kevrekidis, Panayotis G. Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchoŗ azych 2 Cracow30-084 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow30-059 Poland Department of Mathematics and Statistics University of Massachusetts AmherstMA01003-4515 United States

The behavior of the kink in the sine-Gordon (sG) model in the presence of periodic inhomogeneity is studied. An ansatz is proposed that allows for the construction of a reliable effective model with two degrees of freedom. Effective models featuring very good agreement with the original field-theoretic partial differential equation are constructed, including in the non-perturbative region and for relativistic velocities. The numerical solutions of the sG model describing the evolution of the kink in the presence of a barrier as well as in the case of a periodic heterogeneity under the potential additional influence of a switched bias current and/or dissipation were obtained. The results of the field equation and the effective models were favorably compared. The effect of the choice of initial conditions in the field model on the agreement of the results with the effective model is also discussed. © 2024, CC BY.

关键词： sine-Gordon equation

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Computerized tomography

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Drone-assisted adaptive object detection and privacy-preserving surveillance in smart cities using whale-optimized deep reinforcement learning techniques

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Scientific Reports 2025年第1期15卷 1-20页

作者： Abu-Khadrah, Ahmed Al-Qerem, Ahmad Hassan, Mohammad R. Ali, Ali Mohd Jarrah, Muath Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa 13132 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan School of Computing Skyline University College Sharjah United Arab Emirates

Drone/ unmanned aerial vehicles (UAV) surveillance for object/ human detection is familiar in large gatherings in the modern cities era. Artificial intelligence algorithms and computer-aided processing will handle the images extracted from the surveillance videos to reveal the object. This article proposes a novel object detection technique (ODT) that assimilates whale optimization algorithms and deep reinforcement learning. The optimization algorithm detects spreading image features from the origin to the end of x×y pixels. This feature extraction is performed until the complete image pixels are covered to identify their existence in the least position. The forging behaviour of the whales is implied to identify highly overlapping features for object/ human detection/ classification. If the overlapping features increase, the whale’s movement is updated from the last-known highest pixel position. The reinforcement learning recommends that new whale agents validate the low overlapping features, such that the forging whale agent moves towards the large overlapping feature. Therefore, the highest overlapping feature-based pixel differentiation is pursued using forging and searching whales to identify objects through collated pixels. © The Author(s) 2025.

关键词： DRL Feature overlap Object detection Whale optimization

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Effect of representation format on conceptual question performance and eye-tracking measures

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Physical Review Physics Education Research 2023年第2期19卷 020114-020114页

作者： Ana Susac Maja Planinic Andreja Bubic Katarina Jelicic Marijan Palmovic Department of Applied Physics Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia Department of Physics Faculty of Science University of Zagreb Bijenicka 32 10000 Zagreb Croatia Department of Psychology Faculty of Humanities and Social Sciences University of Split Sinjska 2 21000 Split Croatia Laboratory for Psycholinguistic Research Department of Speech and Language Pathology University of Zagreb Borongajska cesta 83h 10000 Zagreb Croatia

Previous studies have shown the important role of different representations in the teaching and learning of physics. In this study, we used eye tracking to investigate the effect of different representations on the process of answering conceptual questions. We compared students’ scores and eye-tracking measures on isomorphic questions which contained graphical, pictorial, and verbal representations. On average, in two-thirds of cases, students were consistent in their answers (correct or incorrect) across all three representations. There was no statistically significant difference in students’ scores for different representations. However, eye-tracking measures suggest that it was easiest for students to extract information from verbal representations and most difficult from pictorial representations for the conceptual questions used in this study. These results could be useful to teachers and researchers when creating conceptual questions and, more generally, when teaching with multiple representations.

关键词： Instructional strategies Scientific reasoning & problem solving

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

引用

2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Leveraging ensemble learning with metaheuristic optimization algorithms for an intelligent cyberattack defense framework in an IoT environment

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Alexandria Engineering Journal 2025年 129卷 103-116页

作者： Hend Khalid Alkahtani Mashael M. Asiri Rakan Alanazi Mohammed Mujib Alshahrani Fahad Alzahrani Shaymaa Sorour Mesfer Al Duhayyim Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Information Systems and Cybersecurity College of Computing and Information Technology University of Bisha P.O. Box 551 Bisha Saudi Arabia Department of Information and Computer Science College of Computing and Mathematics King Fahad University of Petroleum and Minerals Saudi Arabia Department of Management Information Systems College of Business Administration King Faisal University Al-Ahsa 31982 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Ahsa 16273 Saudi Arabia

Cybersecurity continues to be a significant problem for some industries on the Internet, as the number of security breaches is increasing over time. It is identified that many zero-day threats are continuously developing due to the addition of several protocols, mainly from the Internet of Things (IoT). The majority of these attacks are smaller versions of formerly known cyberattacks. IoT cybersecurity aims to decrease cybersecurity risk for companies and users by securing privacy and assets. The expansion of automatic devices for cyber threat classification and detection utilizing artificial intelligence (AI) and deep learning (DL) devices has become necessary for accomplishing security in IoT environments. Due to their notable performance, DL-based methods are essential to successfully reducing security problems associated with IoT gadgets. This article presents a Leveraging Ensemble Learning and Metaheuristic Optimization Algorithms for Intelligent Cyber Attack and Defense Framework (LELMOA-ICADF) model in IoT networks. The main intention of the LELMOA-ICADF model is to deliver an efficient method using advanced ensemble models for enhancing IoT cybersecurity. Initially, the min-max normalization is employed in the data pre-processing stage for transforming input data into a structured format. The cat swarm optimization (CSO) technique is utilized for the feature selection process to choose the most relevant and significant features from the dataset. Furthermore, ensemble models such as the bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) are employed for the attack classification process. Finally, the artificial bee colony (ABC) method is used for parameter tuning to improve the classification performance of ensemble classifiers. The experimental assessment of the LELMOA-ICADF approach is performed under Edge-IIoT and ToN-IoT datasets. The performance validation of the LELMOA-ICADF approach

关键词： Cybersecurity IoT Ensemble models Attack detection Deep learning Artificial bee colony optimization Feature selection

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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