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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Blockchain-Driven Academic Learning Record Management in Higher Education: A Comprehensive Review of Methodologies, Applications, Benefits, and Challenges

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SN computer Science 2025年第5期6卷 1-33页

作者： Chaudhari, Sheetal Shirole, Mahesh Computer Science and Engineering Department Sardar Patel Institute of Technology Andheri Maharashtra Mumbai 400058 India Computer Engineering and Information Technology Department Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai 400031 India

Blockchain technology has the potential to revolutionize the education sector by improving the legitimacy and security of academic procedures. The academic learning record management records the learner’s academic qualifications and achievements. Keeping track of higher education academic learning records is essential to assess students’ credentials and advance their careers. The COVID-19 outbreak highlighted the need for flexible and collaborative learning systems. However, the processes of recording credit accumulation, verification, and transfer are complicated by the disparate credit schemes found in different universities. This creates the need for an academic credit management system. Secure learning record management is necessary because validating the professional and academic accomplishments listed on the resumé currently poses a challenge. Blockchain can remedy this scenario by guaranteeing data privacy, reducing the possibility of hacking, and offering transparent, traceable academic credentials as well as verification of other achievements. Despite these benefits, challenges are still associated with implementing blockchain in academic learning record management. These include handling multiple credentials with a unique identity, absence of smart contracts, on-chain and centralized storage, scalability problems, transaction costs, restricted control over student data, and inadequate student data recording standards. The main objective of this review is to examine recent studies on blockchain-driven academic learning record management by categorizing them into learning record management and academic credit management. This paper identifies research gaps and suggests further studies and practical applications to enhance transparency, efficiency, and trust in the education system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Academic bank of credits Academic credit management Blockchain Education Learning record management NEP 2020

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Uncertainty-Guided Metric Learning Without Labels

Uncertainty-Guided Metric Learning Without Labels

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Devalraju, Dhanunjaya Varma Sekhar, C Chandra Indian Institute of Technology Madras Department of Computer Science and Engineering India

ISBN: (纸本)9798331510831

Unsupervised metric learning aims to learn the discriminative representations by grouping similar examples in the absence of labels. Many unsupervised metric learning algorithms combine clustering-based pseudo-label generation with embedding fine-tuning. However, pseudo-labels can be unreliable and noisy. This could affect metric learning and degrade the quality of the learned representations. In this work, we propose an approach to reduce the negative effect of label noise on learning discriminative embeddings by using context and prediction uncertainty. In particular, we refine the pseudo-labels by aggregating information from neighbors. We propose a function to weigh the pairs, leveraging their prediction confidence and uncertainty. We modify the metric learning loss function to incorporate this weight. Experimental results demonstrate the effectiveness of our proposed method on standard datasets for metric learning. © 2025 IEEE.

关键词： Clustering algorithms

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AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance 5th

AI-Driven Glaucoma Susceptibility Assessment and Lifestyle G...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Talla, Ganesh Venkata Sundar Akhil, Toram Rajeev Thatikonda, Surendra potnuru, Tarun Lalitha, Chirravuri Surya Naga Sai Bonthu, Sridevi Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031770746

This project addresses the global health challenge posed by glaucoma through the development of an innovative Python-based application. Utilizing advanced machine learning techniques, specifically the VGG16 model with transfer learning, the application aims to enhance glaucoma diagnosis and management. By automating glaucoma severity classification into distinct categories, including No_DR, Proliferate_DR, Mild, Moderate, and Severe, the application provides personalized medical advice tailored to the predicted severity. The user-friendly interface, powered by the Tkinter library, enables individuals to upload eye images for real-time analysis, empowering them to actively monitor their eye health. Through early detection and informed decision-making, the application strives to slow down the progression of glaucoma and enhance overall quality of life. This project exemplifies the intersection of technology and healthcare, promising to positively impact lives by promoting early awareness and proactive glaucoma management. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Decision making

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Existence and uniqueness of mean field equilibrium in continuous bandit game

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Science China(Information Sciences) 2025年第3期68卷 395-396页

作者： Xiong WANG Yuqing LI Riheng JIA School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University Wuhan University Shenzhen Research Institute School of Computer Science and Technology Zhejiang Normal University

Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.

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Machine Learning-Based Air Pollution Monitoring and Forecasting

Machine Learning-Based Air Pollution Monitoring and Forecast...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： M, Naga Ravindra Babu Durga Satish, M. Prasanthi, B.V. Jagadeesh, S.V.V.D. Janardhana Naidu, J.N.S.S. Pradeep, Immidi Kali Department of Computer Science & Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031513374

Today, the governments of developing countries are focusing more on air pollution. Air pollution occurs because of factors like fuel use in vehicles such as private transport, industries, and, more importantly, the burning of waste like plastic and grass. These are a few of the elements that contribute to air pollution. All of these factors have an impact on air quality. It can be calculated or determined by using PM2.5 and other variables. If this level is high, then it may be a serious problem for people’s health. To control air pollution, continuous pollution monitoring is required. Here, different machine learning models like linear regression, decision tree classifiers, random forest classifiers, and KNN classifiers are used to detect whether the sample of air is polluted or not. On the basis of previous readings of PM2.5, NH3, CO, NO, NOx, and NO2, SO2 is used to predict the future values of the AQI. By collecting datasets of the daily atmospheric conditions of a specific city or town, this system is able to predict the PM2.5 level and also detect the air pollution status. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Air quality

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Robust Detection of Malicious Encrypted Traffic via Contrastive Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4228-4242页

作者： Shen, Meng Wu, Jinhe Ye, Ke Xu, Ke Xiong, Gang Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Computer Science Beijing100081 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Institute of Information Engineering Chinese Academy of Sciences Beijing100190 China

Traffic encryption is widely used to protect communication privacy but is increasingly exploited by attackers to conceal malicious activities. Existing malicious encrypted traffic detection methods rely on large amounts of labeled samples for training, limiting their ability to quickly respond to new attacks. These methods also are vulnerable to traffic obfuscation strategies, such as injecting dummy packets. In this paper, we propose SmartDetector, a robust malicious encrypted traffic detection method via contrastive learning. We first propose a novel traffic representation named Semantic Attribute Matrix (SAM), which can effectively distinguish between malicious and benign traffic. We also design a data augmentation method to generate diverse traffic samples, which makes the detection model more robust against different traffic obfuscation strategies. We propose a malicious encrypted traffic classifier that first pre-trains a model via contrastive learning to learn deep representations from unlabeled data, then fine-tunes the model with a supervised classifier to achieve accurate detection even with only a few labeled samples. We conduct extensive experiments with five public datasets to evaluate the performance of SmartDetector. The results demonstrate that it outperforms the state-of-the-art (SOTA) methods in three typical scenarios. Specifically, in the evasion attack detection scenario, SmartDetector achieves an F1 score and AUC above 93%, with average improvements of 19.84% and 18.17% over the SOTA method, respectively. © 2005-2012 IEEE.

关键词： Data obfuscation

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Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliteration - Roman to Devanagari using LLaMa 1

Team IndiDataMiner at IndoNLP 2025: Hindi Back Transliterati...

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1st Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, IndoNLP 2025

作者： Kumar, Saurabh Kakadiya, Dhruvkumar Babubhai Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762145

The increasing use of Romanized typing for Indo-Aryan languages on social media poses challenges due to its lack of standardization and loss of linguistic richness. To address this, we propose a sentence-level back-transliteration approach using the LLaMa 3.1 model for Hindi. Leveraging fine-tuning with the Dakshina dataset, our approach effectively resolves ambiguities in Romanized Hindi text, offering a robust solution for converting it into the native Devanagari script. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Clinical deep model to analyse medical multivariate time-series data for health diagnosis

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Cyber-Physical Systems 2025年第2期11卷 139-164页

作者： Almahadeen, Layth Vijay, Richa Shabaz, Mohammad Soni, Mukesh Singh, Pavitar Parkash Patel, Pavan Byeon, Haewon Department of Financial and Administrative Sciences Al- Balqa’Applied University Al-Salt Jordan Department of computer science Amity University UP Noida India Department of Computer Science Engineering Model Institute of Engineering and Technology Jammu Jammu and Kashmir India Department of Computer Science Engineering University Centre for Research & Development Chandigarh University Mohali India Department of Management Lovely Professional University Phagwara India Department of Computer Science Engineering Ahmedabad Institute of Technology Ahmedabad India Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features;feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decision making

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Classification and study of music genres with multimodal Spectro-Lyrical Embeddings for Music (SLEM)

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Multimedia Tools and Applications 2025年第7期84卷 3701-3721页

作者： Mehra, Ashman Mehra, Aryan Narang, Pratik Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Computer Science Department Carnegie Mellon University PittsburghPA United States Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Pilani India

The essence of music is inherently multi-modal – with audio and lyrics going hand in hand. However, there is very less research done to study the intricacies of the multi-modal nature of music, and its relation with genres. Our work uses this multi-modality to present spectro-lyrical embeddings for music representation (SLEM), leveraging the power of open-sourced, lightweight, and state-of-the-art deep learning vision and language models to encode songs. This work summarises extensive experimentation with over 20 deep learning-based music embeddings of a self-curated and hand-labeled multi-lingual dataset of 226 recent songs spread over 5 genres. Our aim is to study the effects of varying the weight of lyrics and spectrograms in the embeddings on the multi-class genre classification. The purpose of this study is to prove that a simple linear combination of both modalities is better than either modality alone. Our methods achieve an accuracy ranging between 81.08% to 98.60% for different genres, by using the K-nearest neighbors algorithm on the multimodal embeddings. We successfully study the intricacies of genres in this representational space, including their misclassification, visual clustering with EM-GMM, and the domain-specific meaning of the multi-modal weight for each genre with respect to ’instrumentalness’ and ’energy’ metadata. SLEM presents one of the first works on an end-to-end method that uses spectro-lyrical embeddings without hand-engineered features. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Music

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