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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Khan, Mansoor Ali Aman, Muhammad Naveed Sikdar, Biplab College of Design and Engineering National University of Singapore Department of Electrical and Computer Engineering 117575 Singapore School of Computing College of Engineering University of Nebraska-Lincoln LincolnNE68588-0115 United States

ISBN: (纸本)9798331508050

The rapid evolution of communication networks to 5G/6G has introduced significant challenges, particularly in ensuring data security, low latency, high throughput, efficient energy consumption, dynamic access to multiple connection types, and managing the influx of connected devices. Quantum communication, leveraging entangled states like the perfect W-state, offers a promising solution with its high degree of entanglement, secure information transmission, and resilience against decoherence. However, practical applications and experimental validations remain limited, especially regarding the integration with Local Operations and Classical Communication (LOCC) protocols. Additionally, optimizing perfect W-state-based communication protocols for the splitting and sharing of quantum information has been scarcely explored. This article presents a Quantum Information Sharing and Splitting (QISS) protocol that integrates perfect W-states with LOCC to enhance 5G and 6G communication. Using the Eagle r3 processor based on superconducting qubits, our experiments demonstrated a fidelity of 0.82 ± 0.02 for the perfect W-state circuit and 0.55 ± 0.03 for the integrated W-state + LOCC in the QISS communication prototype. These findings, quantified through Quantum State Tomography, significantly improve communication security, network densification, and effectiveness. Furthermore, our research addresses existing gaps in quantum communication implementation, paving the way for scalable quantum networks and advanced encryption methods. This work marks a substantial step towards secure and efficient data transmission in next-generation communication systems. © 2025 IEEE.

关键词： Quantum communication

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Optimal Bounds for Distinct Quartics 51

Optimal Bounds for Distinct Quartics

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Computer Science University of Haifa Israel Department of Software Engineering Braude College of Engineering Karmiel Israel

ISBN: (纸本)9783959773225

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms – it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n × n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permuta

关键词： Optimization

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Asthma Disease Risk Prediction Using Machine Learning Techniques

Asthma Disease Risk Prediction Using Machine Learning Techni...

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2023 International Conference on computer Communication and informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Selvaraj, P.A. Kumar, Kamdev Aadarsh, V. Mariya Arockiya Akash, A. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences IT Department Oman

ISBN: (纸本)9798350348217

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method's root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set's asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed. © 2023 IEEE.

关键词： Support vector machines

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Deep-Source EEG Generation with Deep Learning from Different Configurations of Surface EEG

Deep-Source EEG Generation with Deep Learning from Different...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiadao Zou Qingxue Zhang Luddy School of Informatics Computing and Engineering Indiana University Department of Computer Science and Department of Biomedical Engineering Indiana University School of Informatics Computing and Engineering Indiana University Luddy Indianapolis

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology.

关键词： Deep learning Heuristic algorithms Smart healthcare Epilepsy Big Data Electroencephalography Streams Consumer electronics

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Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

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computers, Materials & Continua 2023年第1期74卷 607-621页

作者： Anwer Mustafa Hilal Aisha Hassan Abdalla Hashim Heba G.Mohamed Mohamed K.Nour Mashael M.Asiri Ali M.Al-Sharafi Mahmoud Othman Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University Malaysia 53100 Kuala LumpurMalaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital *** Uniform Resource Locators(URLs)can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their *** may result in compromised security of the systems,scams,and other such *** attacks hijack huge quantities of the available data,incurring heavy financial *** the same time,Machine Learning(ML)and Deep Learning(DL)models paved the way for designing models that can detect malicious URLs accurately and classify *** this motivation,the current article develops an Artificial Fish Swarm Algorithm(AFSA)with Deep Learning Enabled Malicious URL Detection and Classification(AFSADL-MURLC)*** presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine *** attain this,AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding *** addition,the created vector model is then passed onto Gated Recurrent Unit(GRU)classification to recognize the malicious ***,AFSA is applied to the proposed model to enhance the efficiency of GRU *** proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle *** simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

关键词： Malicious URL cybersecurity deep learning machine learning metaheuristics gated recurrent unit

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YoNet: A Neural Network for Yoga Pose Classification

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SN computer Science 2023年第2期4卷 198页

作者： Ashraf, Faisal Bin Islam, Muhammad Usama Kabir, Md Rayhan Uddin, Jasim Department of Computer Science and Engineering University of California Riverside CA United States School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA United States Department of Computing Science University of Alberta Edmonton AB Canada Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff Wales United Kingdom

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. © 2023, The Author(s).

关键词： Deep learning Image classification Neural network Pose recognition Yoga pose

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive computing in engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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