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MXene sensors based on optical and electrical sensing signals:from biological,chemical,and physical sensing to emerging intelligent and bionic devices

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PhotoniX 2023年第1期4卷 438-493页

作者： Leiming Wu Xixi Yuan Yuxuan Tang S.Wageh Omar A.Al-Hartomy Abdullah G.Al-Sehemi Jun Yang Yuanjiang Xiang Han Zhang Yuwen Qin Guangdong Provincial Key Laboratory of Information Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou510006China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai519000China College of Electronics and Information Engineering Shenzhen UniversityShenzhen518061China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) College of Physics and Optoelectronic EngineeringShenzhen Key Laboratory of Micro-Nano Photonic Information TechnologyShenzhen UniversityShenzhen518060China Department of Physics Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia School of Physics and Electronics Hunan UniversityChangsha410082China

Sensing devices are key nodes for information detection,processing,and conversion and are widely applied in different fields such as industrial production,environmental monitoring,and ***,increasing demand of these devices has complicated the application scenarios and diversified the detection targets thereby promoting the continuous development of sensing materials and detection *** recent years,Tin+1CnTx(n=1,2,3)MXenes with outstanding optical,electrical,thermal,and mechanical properties have been developed as ideal candidates of sensing materials to apply in physical,chemical,and biological sensing *** this review,depending on optical and electrical sensing signals,we systematically summarize the application of Tin+1CnTx in nine categories of sensors such as strain,gas,and fluorescence *** excellent sensing properties of Tin+1CnTx allow its further development in emerging intelligent and bionic devices,including smart flexible devices,bionic E-skin,neural network coding and learning,bionic soft robot,as well as intelligent artificial eardrum,which are all discussed briefly in this ***,we present a positive outlook on the potential future challenges and perspectives of MXene-based *** have shown a vigorous development momentum in sensing applications and can drive the development of an increasing number of new technologies.

关键词： Tin+1CnTx(n=1 2 3)MXenes Sensing applications Intelligent devices

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Individual Privacy Protection in Cybersecurity Using data Analytics and Machine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Arun Bhavadharani K B. R. Supreeth Sumit Pundir K Pradeepa K. Abdul Razak Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamil Nadu India Department of Computer Science Oxford Degree and PG College Bangalore India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu Department of Mathematics K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises.

关键词： Support vector machines data privacy data analysis Accuracy Scalability Telecommunication traffic data collection Privacy breach Protection Computer security

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

arXiv

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arXiv 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Zhang, Yue Wang, Ren Shi, Xiaoshuang Xu, Kaidi Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People

A Machine Learning Model for Augmenting the Media Accessibil...

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Communication, Security and artificial intelligence (ICCSAI), International Conference on

作者： Hari Krishnan Andi S. Senbagam Dilip Kumar Sharma Mayura Shelke N K Senthil Kumar Ramesh Chandra Poonia Centre for Postgraduate Studies Asia Metropolitan University Malaysia Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nandu India Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science AISSMS IOIT Pune India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India Department of Computer Science CHRIST (Deemed to be University) Delhi-NCR Ghaziabad Uttar Pradesh India

In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called “Fast Captioning (FC)” to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate.

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Development of Elliptical Cryptography Technique to Watermark Embedded and Extrusion for Healthcare Records 2nd

Development of Elliptical Cryptography Technique to Watermar...

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2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Hegde, N. Prajwal Sivaraman, R. Dharmamoorthy, G. Dubey, Ankit Kumar Patro, Pramoda Mary, S. Suma Christal Department of Artificial Intelligence and Data Science NMAM Institute of Technology Nitte Deemed to be University Karkala India Department of Mathematics Dwaraka Doss Goverdhan Doss Vaishnav College Arumbakkam Tamil Nadu Chennai India Department of Pharmaceutical Analysis MB School of Pharmaceutical Sciences Mohan Babu University Tirupati Rangampeta India Department of Computer Science and Engineering Baderia Global Institute of Engineering and Management MP Jabalpur India Department of Mathematics Koneru Lakshmaiah Education Foundation Hyderabad India Department of Information Technology Panimalar Engineering College Poonamalle Chennai India

ISBN: (纸本)9783031734939

An application of E-healthcare watermarking method based on a hybridization of encryption as well as compression algorithms is proposed as the primary goal of this work. One step involves inserting a watermark into the image, and the other involves removing it. These are the two main components of the suggested system. Before beginning the embedding procedure, use a region-growing method to segment the tumour independently. Hash Secure Algorithm-256 encrypt the electronic health record (EHR) and Elliptical curve cryptography and its associated area of interest. Mathematical coding algorithms are then used to concatenate and compress the data. Lastly, the packed down bit is embedded into the original picture. When extracting a substance, the same steps are conducted again. Using the maximum signal ratio to noisy and normalized correlation, the experimental outcomes for various medicinal photos using EHR are shown, and the efficacy of the suggested method is evaluated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Machine Learning Model Based on Gary-Level Co-occurrence Matrix for Chest Sarcoidosis Diagnosis

Machine Learning Model Based on Gary-Level Co-occurrence Mat...

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Electrical, Energy, and artificial intelligence (EICEEAI), International engineering Conference on

作者： Hani Attar Ahmed Solyman Mohanad A. Deif Mohamed Hafez Hager M. Kasem Abd-Elnaser Fawzy Mohamed Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Engineering and Built Environment School of Computing Glasgow Caledonian University Glasgow UK Department of Artificial Intelligence College of Information Technology Misr University for Science & Technology (MUST) October 6 City Egypt Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo Egypt Communication Department Bilbies Higher Institute for Engineering Sharqia Egypt

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Sarcoidosis is often misdiagnosed and mistreated due to the limitations of radiological presentations. With the recent emergence of COVID-19, doctors face challenges distinguishing between the symptoms of these two diseases. As a result, people are adapting to new practices such as working from home, wearing masks, and using disinfectants. The similarity in symptoms between sarcoidosis and COVID-19 has made it difficult to differentiate between the two conditions, potentially impacting patient outcomes. The diagnostic process for distinguishing between them is time-consuming, labor-intensive, and costly. Researchers and medical practitioners have gained significant attention to computer-aided detection (CAD) systems for sarcoidosis using radiological images to address this issue. This study uses machine learning classifiers, ensembles, and features such as Gray-Level Co-occurrence Matrix (GLCM) and histogram analysis to identify lung sarcoidosis infection from chest X-ray images. The proposed method extracts statistical texture features from X-ray images by calculating a GLCM for each image using various stride combinations. These GLCM features are then used to train the machine learning classifiers and ensembles. The research focuses on multi-class classification, categorizing X-ray images into three classes: sarcoidosis-affected, COVID-19-affected, and regular lungs, as well as binary classification, distinguishing sarcoid-affected cases from others. The proposed method, known for its simplicity and computational efficiency, demonstrates significant accuracy in identifying sarcoidosis and COVID-19 from chest X-ray images.

关键词： COVID-19 Histograms Solid modeling Design automation Accuracy Lung Machine learning

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Agile Meets Quantum: A Novel Genetic Algorithm Model for Predicting the Success of Quantum Software Development Project

arXiv

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arXiv 2024年

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta53851 Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Methodology: Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project. © 2024, CC BY.

关键词： Genetic algorithms

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AI POWERED Cryptography for Secure Machine-to-Machine (M2M) Communication in IoT Networks

AI POWERED Cryptography for Secure Machine-to-Machine (M2M) ...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： C Balarama Krishna Saai Krishnan Udayakumar Sri Bhargav Krishna Adusumilli Chinnem Rama Mohan T. Keerthika Pankaj Mudholkar School of CS & AI SR Universtity Warangal Telangana Salesforce Inc. Bellevue WA Bellevue WA USA Mindquest Technology Solutions llc Phoenix Arizona USA Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The Internet of Things' (IoT) next generation of computing depends on mobile ad hoc networks, or MANETs. In a MANET, every device has the ability to move in any direction. By offering cloud services to MANET subscribers, data processing and resource management can be managed across each element, enabling users to access smart devices within the IoT framework. MANET is vulnerable to a variety of attacks that reduce network performance. Only active network layer assaults are the subject of this study. We have implemented the SAODV protocol to improve security and performance in MANETs, and our solution makes use of hybrid cryptography techniques (RSA and DES algorithms) on SAODV. This study compares the AODV and SAODV routing protocols of MANETs using simulations of several factors, including energy, packet delivery ratio, and throughput. The suggested method can improve communication network security and identify new positions by employing a Machine-To-Machine (SD-M2M) approach. The NS2 network simulation environment is used to execute the suggested cryptographic routing method. The proposed method achieves high throughput, high packet delivery ratio, and low energy usage compared to the traditional method.

关键词： Performance evaluation Machine-to-machine communications Routing Throughput Ad hoc networks Routing protocols Cryptography Internet of Things Mobile computing AODV

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Ensuring Web Application Security: An OWASP Driven Development Methodology

Ensuring Web Application Security: An OWASP Driven Developme...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： M. Sakthivel S. Sivanantham N. Bharathiraja N. Bala Krishna R. Kamalraj V. Saravana Kumar Department of Artificial Intelligence and Data science Sri Shanmugha College of Engineering and Technology Sankari Salem India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Biomedical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of AI&ML School of Computing Mohan Babu University Tirupati Andhra Pradesh India School of CS and IT Jain (Deemed to be) University Jayanagar Bangalore India Department of IT Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Website security is a big threat for both large companies and individual developers, the more advanced the technology employed, the more challenging it is to build secure website development procedures. Hackers can easily access vulnerabilities that were not patched during development and that were thereafter distributed. This could result in significant financial losses for the business or the person. Users who visit vulnerable websites may be subject to XSS attacks that could compromise their systems, or an unsecure database configuration may result in a potential data leak, in which case the password of every user who has registered on the website is compromised. Users who use the same password on numerous websites run the danger of identity theft. This study offers the most common vulnerabilities that should be fixed in any application, giving developers the crucial place to start. The overwhelming number of vulnerabilities in each application that is being developed served as the impetus for this project. Every developer, regardless of expertise level, needs a place to start in order to fix any vulnerabilities that may have appeared in their program. The goal of this project is to create a safe online application in accordance with the recommendations of the Open Web Application Security Project (OWSAP). This demonstrates how web application vulnerabilities can be mitigated through coding changes, configuration adjustments, and patch applications. The top ten vulnerabilities according to OWASP are SQL injections, faulty authentication, sensitive data exposure, faulty access control, and XML external entities. The web application’s security has been examined and shown to have defenses in place for those vulnerabilities.

关键词： Knowledge engineering databases Computer hacking Identity theft XML Passwords Companies

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Inference-based time-resolved stability analysis of nonlinear whole-cortex modeling: application to Xenon anaesthesia

Inference-based time-resolved stability analysis of nonlinea...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yun Zhao Mario Boley Andria Pelentritou William Woods David Liley Levin Kuhlmann Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton Australia Laboratoire de Recherche en Neuroimagerie (LREN) University Hospital (CHUV) and University of Lausanne (UNIL) Lausanne Switzerland School of Health Sciences Swinburne University of Technology Hawthorn Australia Department of Medicine-St Vincent’s Hospital The University of Melbourne Parkville Australia

This study characterizes the neurophysiological mechanisms underlying electromagnetic imaging signals using stability analysis. Researchers have proposed that transitions between conscious awake and anaesthetised states, and other brain states more generally, may result from system stability changes. The concept of stability in dynamical systems theory provides a mathematical framework to describe this possibility. In particular, the degree to which a system’s trajectory in phase space is affected by small perturbations determines the stability. Previous studies using linear or oscillator-based whole-brain models cannot represent complex cerebrocortical dynamics, or model parameters were pre-assumed or inferred from data but did not change over time. This study proposes a nonlinear neurophysiologically plausible whole-cortex modeling framework to analyze the stability of brain dynamics for the emergence and disappearance of consciousness using time-varying parameters estimated from the *** relevance— Depth of anaesthesia is typically measured through changes in EEG statistics like the bispectral index and spectral entropy. However, these monitors have been found to fail in preventing awareness during surgery and postoperative recall. Our whole-cortex stability analysis may be useful in measuring anaesthesia levels in clinical settings, as it changes with the level of consciousness and is independent of individual differences and anaesthetic agents. The proposed method can also be used to, for example, identify critical brain regions for consciousness, locate the epileptogenic zone and investigate the dominance of extrinsic or intrinsic factors in brain functions.

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