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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

computer Modeling in Engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Hierarchical Text Classification Using Language Models with Global Label-Wise Attention Mechanisms 4th

Hierarchical Text Classification Using Language Models with...

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4th Southern African Conference for Artificial Intelligence Research, SACAIR 2023

作者： du Toit, Jaco Dunaiski, Marcel Department of Mathematical Science Computer Science Division Stellenbosch University Stellenbosch South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

ISBN: (纸本)9783031490019

Hierarchical text classification (HTC) is a natural language processing task with the objective of categorising text documents into a set of classes from a structured class hierarchy. Recent HTC approaches combine encodings of the class hierarchy with the language understanding capabilities of pre-trained language models (PLMs) to improve classification performance. Furthermore, label-wise attention mechanisms have been shown to improve performance in HTC tasks by placing larger weight on more important parts of the document for each class individually to obtain label-specific representations of the document. However, using label-wise attention mechanisms to fine-tune PLMs for downstream HTC tasks has not been comprehensively investigated in previous work. In this paper, we evaluate the performance of a HTC approach which adds label-wise attention mechanisms, along with a label-wise classification layer, to a PLM which is subsequently fine-tuned on the downstream HTC dataset. We evaluate several existing label-wise attention mechanisms and propose an adaptation to one of the approaches which separates the attention mechanisms for the different levels of the hierarchy. Our proposed approach allows the prediction task at a certain level to leverage the information gained from the predictions performed at all ancestor levels. We compare the different label-wise attention mechanisms on three HTC benchmark datasets and show that our proposed approach generally outperforms the other label-wise attention mechanisms. Furthermore, we show that without using the complex techniques proposed in recent HTC approaches, our relatively simple approach outperforms state-of-the-art approaches on two of the three benchmark datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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AMulti-Layers Information Fused Deep Architecture for Skin Cancer Classification in Smart Healthcare

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computers, Materials & Continua 2025年第6期83卷 5299-5321页

作者： Veena Dillshad Muhammad Attique Khan Muhammad Nazir Jawad Ahmad Dina Abdulaziz AlHammadi Taha Houda Hee-Chan Cho Byoungchol Chang Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-Khobar31952Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center for Computational Social Science Hanyang UniversitySeoul01000Republic of Korea Department of Computer Science Hanynag UniversitySeoul01000Republic of Korea

Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient *** evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task *** recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in *** learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and *** this work,we proposed a novel deep architecture for skin cancer classification in innovative *** proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected *** proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter *** the first model,four residual blocks are added in a squeezed fashion with a small filter *** the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion *** make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is *** training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance *** final features are passed on to the classifiers,and classification results are ***,the proposed trained model is interpreted through LIME-based localization on the HAM10000 *** experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and *** obtained an improved accuracy of 90.8%and 99.3%on these datasets,***,the proposed architecture returned 91.6%for the cancer *** conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art

关键词： Smart health skin cancer internet of things deep learning residual blocks fusion optimization

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Improving Antibody-Antigen Interaction Prediction Through Flexibility with ESMFold

Improving Antibody-Antigen Interaction Prediction Through Fl...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Joubbi, Sara Maccari, Giuseppe Ciano, Giorgio Micheli, Alessio Milazzo, Paolo Medini, Duccio Department of Computer Science University of Pisa Pisa Italy Data Science for Health Lab Fondazione Toscana Life Sciences Siena Italy

Antibodies are essential proteins in the immune system due to their capacity to bind to specific antigens. They also play a critical role in developing vaccines and treatments for infectious diseases. Their complex structure, with variable regions for antigen binding and flexible hinge regions, presents challenges for accurate computational modeling. Recent advancements in deep learning have revolutionized protein structure prediction. Despite these advancements, predicting interactions between antibodies and antigens remains challenging, mainly due to the flexibility of antibodies and the dynamic nature of binding events. This study uses fingerprint-based methodologies that incorporate ESMFold confidence scores as a flexibility feature to model Ab-Ag interactions. Our methodology shows how including flexibility has improved Ab-Ag interactions by 3%, achieving an AUC-ROC of 91%. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Vaccines

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A Python-Based Mixed Discrete-Continuous Simulation Framework for Digital Twins 1

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11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2021

作者： Karanjkar, Neha Joshi, Subodh M. Indian Institute of Technology Goa Ponda India Department of Computational and Data Sciences Indian Institute of Science Banglore Banglore India

ISBN: (数字)9783031231490

ISBN: (纸本)9783031231483

The use of Digital Twins is set to transform the manufacturing sector by aiding monitoring and real-time decision making. For several applications in this sector, the system to be modeled consists of a mix of discrete-event and continuous processes interacting with each other. Building simulation-based Digital Twins of such systems necessitates an open, flexible simulation framework which can support easy modeling and fast simulation of both continuous and discrete-event components, and their interactions. In this paper, we present an outline and key design aspects of a Python-based framework for performing mixed discrete-continuous simulations. The continuous processes in the system are assumed to be loosely coupled to other components via pre-defined events. For example, a continuous state variable crossing a threshold may trigger an external event. Similarly, external events may lead to a sudden change in the trajectory, state value or boundary conditions in a continuous process. We first present a systematic events-based interface using which such interactions can be modeled and simulated. We then discuss implementation details of the framework along with a detailed example. In our implementation, the advancement of time is controlled and performed using the event-stepped engine of SimPy (a popular discrete-event simulation library in Python). The continuous processes are modelled using existing frameworks with a Python wrapper providing the events interface. We discuss possible improvements to the time advancement scheme, a roadmap and use cases for the framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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引用

25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yagi, Ryuichi Naoi, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Numerous fields employ correlation-based searches to provide insights about relationships in a database. Herein we propose a correlated DNA subsequence search for DNA databases to extract DNA subsequences that frequently co-occur with a query. Typical search methods must evaluate the entire database for potential candidates, which is computationally expensive. To overcome this, we propose a threshold-based algorithm to achieve efficient computation. The proposed algorithm extracts all DNA subsequences whose correlation is more significant than the threshold and excludes unlikely candidates. Experiments involving real-world DNA databases show that the proposed method achieves a faster search than baseline algorithms while maintaining a high accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DNA

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引用

25th International Conference on Information Integration and Web Intelligence, iiWAS 2023

作者： Yasuda, Yuma Shiokawa, Hiroaki Department of Computer Science University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031483158

Various fields from biomedicine to sports science employ similarity searches in multivariate time series (MvTS) to identify patterns and trends. Given MvTS and a query time series, a similarity search extracts a set of subsequences in the MvTS that yield the most significant similarity to the query. Although this is a fundamental task, similarity searches become computationally expensive as the length of the MvTS increases. Herein an efficient method is proposed to accelerate the search process. Using hash-based indexes for MvTS, our proposed method effectively approximates the similarity of each subsequence pair, enhancing the computational efficiency. An experiment involving synthetic and real-world MvTS datasets demonstrates that our proposed method has a higher efficiency and search accuracy compared to existing approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Time series

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