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A Secure and Efficient Information Authentication Scheme for E-Healthcare System

computers, Materials & Continua 2023年第9期76卷 3877-3896页

作者： Naveed Khan Jianbiao Zhang Ghulam Ali Mallah Shehzad Ashraf Chaudhry Faculty of Information Technology Beijing University of TechnologyBeijing100124China Department of Computer Science Shah Abdul Latif UniversityKhairpur66111Pakistan Department of Computer Engineering Faculty of Engineering ArchitectureNisantasi UniversityIstanbul34398Turkey

The mobile cellular network provides internet connectivity for heterogeneous Internet of Things(IoT)*** cellular network consists of several towers installed at appropriate locations within a smart *** cellular towers can be utilized for various tasks,such as e-healthcare systems,smart city surveillance,traffic monitoring,infrastructure surveillance,or sidewalk *** is a primary concern in data broadcasting,particularly authentication,because the strength of a cellular network’s signal is much higher frequency than the associated one,and their frequencies can sometimes be aligned,posing a significant *** a result,that requires attention,and without information authentication,such a barrier cannot be ***,we design a secure and efficient information authentication scheme for IoT-enabled devices tomitigate the flaws in the e-healthcare *** proposed protocol security shall check formally using the Real-or-Random(ROR)model,simulated using ProVerif2.03,and informally using pragmatic *** comparison,the performance phenomenon shall tackle by the already result available in the MIRACL cryptographic lab.

关键词： IoT-enable device e-healthcare authentication edge computing

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Navigating the Nexus: Exploring the Fusion of AI and Nanotechnology for Cutting-Edge Advances 1

Navigating the Nexus: Exploring the Fusion of AI and Nanotec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Izanker, Sakshi V. Dhole, Aman Kumar, Praveen Faculty of Engineering and Technology Department of Computer and Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The convergence of artificial intelligence (AI) and nanotechnology has initiated a transformative journey, introducing innovative possibilities across various fields. This review article explores the dynamic interaction between these two disciplines, emphasizing their collaborative potential, notable accomplishments, and prevailing challenges. The integration of AI algorithms with nanotechnology has expedited material discovery, nanoscale characterization, personalized medical applications, and nano- electronics. AI's predictive capabilities have revolutionized the discovery of novel nanomaterials with customized properties, the enhancement of drug delivery systems, and the design of intricate nano-electronic components. However, this synergy is accompanied by challenges such as data quality, multidisciplinary cooperation, ethical considerations, and computational complexity. The fusion of AI and nanotechnology is poised to transform industries, unlock quantum advancements, and influence sustainable materials. As AI's capacities evolve, scholars are prepared to address challenges and harness the transformative potential of AI- nanotechnology integration. © 2023 IEEE.

关键词： medical applications

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A Framework for Promoting Passive Breast Cancer Monitoring: Deep Learning as an Interpretation Tool for Breast Thermograms

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Iranian Journal of medical Physics 2024年第4期21卷 237-248页

作者： Firouzmand, Mohamad Majidzadeh, Keivan Jafari, Maryam Haghighat, Shahpar Esmaeili, Rezvan Moradi, Leila Misaghi, Nima Ensafi, Mahsa Batmanghelich, Fatemeh Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Biomedical engineering Department Iranian Research Organization for Science & Technology Tehran Iran Breast Cancer Research Center Motamed Cancer Institute ACECR Tehran Iran Department of Computer Engineering Faculty of Engineering Islamic Azad University E-Campus Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran

Introduction: Several types of cancer can be detected early through thermography, which uses thermal profiles to image tissues in recent years, thermography has gained increasing attention due to its non-invasive and radiation-free nature. There is a growing need for thermographic images of breast cancer lesions in different nationalities and ages to develop this technique, however. This study aims to introduce a dataset of breast thermograms. Material and Methods: In this study, thermographic images of breast cancer from Iranian samples were prepared and confirmed due to the limited number of breast thermogram databases. The prepared database was tested using artificial intelligence and another well-known DMR database (Database for Mastology Research) in this study to determine its reliability. Results: A variety of deep learning architectures and transfer learning are used to evaluate these databases for accuracy, sensitivity, speed, training compliance, and validation compliance. According to best-fitted structures for both types of databases, the database obtained from this study has a quality comparable to the DMR reference database, with minimum accuracy, sensitivity, specificity, precision, and F-score of 80%, 86%, 86%, 88%, and 87%, respectively. Conclusion: Using thermography as a method of early breast screening is demonstrated to be effective. In comparison to DMR, the lower statistics of the proposed database (between 2 and 7 percent) indicates that more diverse breast thermograms should be captured in conjunction with improvements to imaging equipment as well as adherence to thermography recording protocols in order to improve the reliability and efficiency of the database. © (2024), (Mashhad University of medical sciences). All rights reserved.

关键词： medical imaging

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Advances in magnesium-based bioresorbable cardiovascular stents: Surface engineering and clinical prospects

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Journal of Magnesium and Alloys 2025年第3期13卷 948-981页

作者： Ganesh Kumar Subham Preetam Arunima Pandey Nick Birbilis Saad Al-Saadi Pooria Pasbakhsh Mikhail Zheludkevich Poovarasi Balan Department of Chemical Engineering School of EngineeringMonash University Malaysia47500Bandar SunwaySelangorMalaysia Faculty of Science Engineering and Built EnvironmentDeakin UniversityWaurn PondsVICAustralia Department of Mechanical&Aerospace Engineering and Department of Chemical&Biological Engineering Monash UniversityVictoria 3800Australia Department of Infrastructure Engineering Faculty of Engineering and Information TechnologyThe University of MelbourneMelbourneVictoria 3010Australia Institute of Surface Science Helmholtz-Zentrum HereonGeesthacht21502Germany Medical Engineering and Technology Hub School of EngineeringMonash University Malaysia Department of Mechanical Engineering School of EngineeringMonash University Malaysia47500Bandar SunwayMalaysia

Magnesium(Mg)-based bioresorbable stents represent a potentially groundbreaking advancement in cardiovascular therapy;offering tem-porary vessel support and complete biodegradability—addressing limitations of traditional stents like in-stent restenosis and long-term ***,challenges such as rapid corrosion and suboptimal endothelialisation have hindered their clinical *** review highlights the latest breakthroughs in surface modiﬁcation,alloying,and coating strategies to enhance the mechanical integrity,corrosion resistance,and biocompatibility of Mg-based *** surface engineering techniques,including polymer and bioactive coatings,are ex-amined for their role in promoting endothelial healing and minimising inﬂammatory *** directions are proposed,focusing on personalised stent designs to optimize efﬁcacy and long-term outcomes,positioning Mg-based stents as a transformative solution in interventional cardiology.

关键词： Magnesium alloy Cardiovascular stent Surface modification Corrosion Biocompatibility Biomedical application

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Network Bandwidth Optimization Using Software Defined Networking (SDN) 5

Network Bandwidth Optimization Using Software Defined Networ...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Haidar, Gaby Abou Zangana, Ali Noaman Khasro Owayjan, Michel Daou, Roy Abi Zeid Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Beriut Lebanon France La Sagesse University Faculty of Engineering Beirut Lebanon

ISBN: (纸本)9798331519957

This paper provides a detailed comparison of traditional networking architectures and Software Defined Networking (SDN) approaches, with a focus on bandwidth optimization and traffic management. Simulations were conducted under various networking scenarios, utilizing Cisco Packet Tracer for traditional networks and Mininet with the OpenDaylight (ODL) controller for SDN. The evaluated scenarios included single, dual, and triple PC setups, as well as high-Traffic conditions. Traditional networks demonstrated packet loss rates of up to 5%, limited bandwidth utilization (~2.443%), and increasing latency as more devices were added. In contrast, SDN-based setups showed no packet loss and significantly higher bandwidth utilization, reaching up to 94.478%. This study highlights the potential of SDN to enhance network efficiency and scalability, addressing a gap in the literature through scenario-based comparative analysis. Future research directions include exploring large-scale topologies, security enhancements, and advanced optimization techniques. © 2025 IEEE.

关键词： Packet loss

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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Beyond Precision and Recall: Measuring Search Engine Consistency Using Rank Stability

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IEEE Access 2025年 13卷 92242-92259页

作者： Lakshmi, S. Thinakaran, Rajermani Batri, Krishnan Sowrirajan, R. Murugan, Sivaram Jain University Department of Electronics and Communication Engineering Karnataka Bengaluru India INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Jain University School of Computer Science and Engineering Karnataka Bengaluru India Dr. N.G.P. Arts and Science College Department of Mathematics Tamil Nadu Coimbatore India Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Department of Computer Engineering Sivas58000 Turkey

Traditional information retrieval metrics such as precision, recall, and F-measure assess document relevance but fail to capture the stability of search rankings. Search engines frequently update their algorithms, leading to variations in retrieved documents and their rankings across identical queries. This study introduces a novel rank correlation measure that quantifies search result consistency by analyzing rank fluctuations in repeated queries, serving as a proxy for temporally separated searches. The proposed approach distinguishes between overlapping and non-overlapping documents, providing a detailed measure of ranking stability. Empirical evaluations comparing Google and Bing reveal that Google exhibits greater rank fluctuations for non-overlapping documents, whereas both search engines maintain relatively stable rankings for overlapping documents. These findings highlight the importance of rank-based evaluation frameworks in search engine analysis, extending beyond conventional retrieval metrics. By incorporating stability assessments, this research enhances the methodology for evaluating search consistency in dynamic web environments, contributing to process innovation in information retrieval (SDG 9: Industry, Innovation, and Infrastructure) and promoting fair, reliable access to information (SDG 16: Peace, Justice, and Strong Institutions). © 2013 IEEE.

关键词： Online searching

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Energy Constrained User-Selection Framework for Subscribed and Non-Subscribed Cooperative MISO-NOMA IoT Network 7

Energy Constrained User-Selection Framework for Subscribed a...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Zaman, Naeem Uz Hassan, Ahmad Kamal Abbas, Ziaul Haq Abbas, Ghulam Gik Institute of Engineering Sciences and Technology Faculty of Engineering Sciences Topi Pakistan Gik Institute of Engineering Sciences and Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798331534271

Non-orthogonal multiple access is a feasible radio access solution for cellular networks to assist Internet of Things devices due to scalable connectivity, higher throughput and low latency. In this work, a user selection strategy is proposed based on subscribed and non-subscribed user scenario while the analysis has been carried out on the harvested energy of each user in the considered Rayleigh fading based downlink multiple input single-output system. Specifically, we have considered two performance metrics namely, outage probability (OP) and energy harvesting (EH) capability. We demonstrate that the user proposed user selection scheme is efficient both in terms of OP and EH, i.e., on average a minimum-maximum difference in EH between selected and non-selected user is approximately 40% and 200%, respectively. Finally, Monte-Carlo simulations are done to validate the expressions obtained for OP and energy against different parameters. © 2024 IEEE.

关键词： Rayleigh fading

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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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